Fact-checked by Grok 2 weeks ago

Defeasible reasoning

Defeasible reasoning is a form of non-monotonic inference in which conclusions are rationally justified by premises yet remain provisional, capable of being defeated or retracted upon the introduction of new evidence that rebuts the conclusion or undercuts the supporting inference, in contrast to deductive reasoning where conclusions are indefeasibly entailed.^[1] This approach models everyday cognitive processes where default assumptions guide decisions amid incomplete information, such as presuming safety in routine activities unless specific risks emerge.^[1] Emerging from mid-20th-century philosophical inquiries into epistemology and legal concepts of prima facie obligations, defeasible reasoning challenged the traditional insistence on deductive validity for sound arguments, with early contributions from H.L.A. Hart in legal philosophy and Roderick Chisholm in epistemology.^[1] John L. Pollock advanced a comprehensive theory in the late 20th century, formalizing it through inference graphs that track arguments, defeaters, and warrants, and implementing it in AI systems like OSCAR to handle perception, induction, and the frame problem in commonsense reasoning.^[1]^[2] Independently, Donald Nute developed defeasible logic as a rule-based framework distinguishing strict rules, defeasible rules, and defeaters, enabling efficient computation of provability in non-monotonic domains.^[3] In applications, defeasible reasoning underpins legal argumentation by accommodating incomplete evidence and overriding precedents with exceptional facts, as well as AI knowledge representation where monotonic logics fail to capture belief revision.^[1] Its defining characteristic lies in balancing evidential support with vulnerability to defeat, fostering robust models of rational agency without requiring exhaustive prior knowledge.^[2]

Core Concepts

Definition and Characteristics

Defeasible reasoning constitutes a form of inference where arguments provide provisional justification that is rationally persuasive but lacks deductive validity, permitting conclusions to be retracted or overridden by newly acquired information.^[1] This process relies on prima facie reasons—initial grounds for belief that hold absent countervailing evidence—and incorporates defeaters, which are facts or arguments that either rebut the conclusion directly (rebutting defeaters) or undermine the supporting reasons (undercutting defeaters).^[4] Central to defeasible reasoning is its non-monotonic character: unlike monotonic systems where entailments persist with added premises, defeasible conclusions may be invalidated by supplementary data, reflecting real-world scenarios of incomplete knowledge and default assumptions.^[5] For instance, observing a shape's rectangular appearance from a distance offers defeasible grounds for inferring it is a rectangle, but closer inspection revealing curvature introduces an undercutting defeater that defeats the inference without contradicting the initial observation.^[1] This mechanism enables handling exceptions, defaults, and probabilistic-like judgments in domains such as commonsense reasoning, legal argumentation, and scientific hypothesis testing, where absolute certainty is unattainable.^[6] Defeasible reasoning thus prioritizes synchronic defeasibility, wherein the current belief set determines justification at any moment, allowing dynamic revision without requiring global inconsistency resolution.^[1] It contrasts with inductive or abductive methods by emphasizing structured defeat over mere probabilistic weighting, though it shares their fallibility; empirical applications, as in automated reasoning systems, demonstrate its utility in managing conflicting priors through prioritized rules or argumentation frameworks.^[6]

Nature of Defeasibility

Defeasibility denotes the capacity of an inference to be rationally compelling based on current premises yet susceptible to defeat by supplementary evidence that either contradicts the conclusion or undermines the inferential connection. This property arises because defeasible arguments incorporate default assumptions or presumptions—provisional rules that apply unless exceptional counterevidence emerges—rather than strict entailment.^[7] Unlike deductive inferences, where the conclusion follows inescapably from the premises and remains indefeasible, defeasible conclusions permit revision without necessitating retraction of the original premises, enabling adaptive reasoning under incomplete information.^[7]^[1] Defeaters in defeasible reasoning fall into two primary categories: rebutting defeaters, which provide direct evidence against the conclusion (e.g., observing a non-flying bird rebuts the inference that a given bird flies), and undercutting defeaters, which erode the reliability of the inferential rule itself without denying the conclusion (e.g., evidence that the bird's wings are clipped undercuts the default assumption linking birdhood to flight capability).^[1] These mechanisms reflect the non-monotonic nature of defeasible inference, where adding premises can retract prior support for a conclusion, contrasting with monotonic logics where expansions preserve validity.^[1] Defeasibility further distinguishes between synchronic and diachronic forms. Synchronic defeasibility involves defeat by evidence available but not previously considered at the time of inference, allowing immediate reassessment within the same evidential context. Diachronic defeasibility, by contrast, stems from genuinely novel information acquired later, which retrospectively invalidates the earlier conclusion.^[1] In epistemological applications, this vulnerability manifests as exposure to propositional or mental-state defeaters, propositions or doxastic states that block or diminish justification, thereby preventing beliefs from attaining knowledge even if initially warranted.^[8]

Distinction from Monotonic Reasoning

Monotonic reasoning, as formalized in classical logics, exhibits the property of monotonicity: if a set of premises Γ entails a conclusion φ (denoted Γ ⊢ φ), then any superset of Γ, such as Γ ∪ {ψ} for an additional premise ψ, also entails φ.^[9] This preservation ensures that expanding the knowledge base does not retract prior deductions, providing a stable foundation for mathematical proofs and formal verification where absolute validity is required.^[9] Defeasible reasoning, by contrast, violates monotonicity, allowing conclusions to be defeated or revised when new information introduces exceptions or counterevidence.^[5] For instance, a default inference like "birds fly" may hold provisionally but can be overridden by specifics such as "penguins do not fly," retracting the generalized conclusion without contradiction in the overall system.^[10] This non-monotonic character models real-world inference patterns, such as legal presumptions or commonsense defaults, where inferences are tentative and subject to defeat rather than irrevocable.^[11] The core distinction lies in the treatment of uncertainty and incompleteness: monotonic systems assume complete, exception-free premises for unassailable conclusions, while defeasible approaches explicitly accommodate partial knowledge, enabling dynamic belief revision essential for adaptive decision-making.^[5] Formalizations of defeasible reasoning thus require mechanisms like defeaters or priorities to resolve conflicts, absent in monotonic frameworks.^[10]

Formal Foundations

Non-Monotonic Logics

Non-monotonic logics formalize defeasible reasoning by allowing the consequence relation to shrink upon the addition of new premises, contrasting with the expansion-only property of monotonic systems. In these logics, a theory's entailed conclusions can be invalidated by further information, enabling the modeling of defaults, exceptions, and incomplete knowledge. This approach addresses limitations in classical logic for real-world inference, where assumptions like "birds fly" hold provisionally until contradicted by specifics such as penguins.^[12] Default logic, introduced by Raymond Reiter in 1980, exemplifies this paradigm through default theories defined as pairs (W, D), where W comprises classical first-order formulas as factual premises, and D consists of defaults of the form \alpha : \beta_1, \dots, \beta_n / \gamma. Here, \alpha is the prerequisite, the \beta_i are consistency conditions checked against the theory's extensions, and \gamma is the consequent derived if \alpha holds and no \beta_i is inconsistent with the resulting belief set. An extension of the theory is a fixed point set S such that W \cup \{\gamma \mid \exists applicable default leading to \gamma\} generates exactly the theorems of S, capturing multiple possible belief revisions based on default applications. Reiter's framework resolves issues like conflicting defaults via semi-normalized forms, where \gamma \supset \bigwedge \beta_i, ensuring applicability only if the consequent aligns with consistency checks; empirical evaluations in AI implementations confirm its utility for handling prioritized exceptions, though it permits multiple extensions requiring selection mechanisms.^[12]^[13] Circumscription, developed by John McCarthy in 1980, provides an alternative via second-order minimization of predicate extensions to embody the "circumstance" or "ceteris paribus" assumption that abnormal entities are scarce unless specified otherwise. For a theory T and predicate P, circumscription selects models minimizing the set of P-objects, formalized as \forall x (P(x) \supset \phi(x)) \land \forall x (\phi(x) \supset P(x)) \land \min \{P\}, where \phi defines fixed properties and minimization prioritizes models with fewer P-instances. This entails defaults like "all birds fly" by circumscribing an "abnormal" predicate for non-flying birds, retracting the conclusion for known exceptions like penguins while preserving it elsewhere; McCarthy's predicate completion variant integrates it with programs, as in situation calculus for action preconditions, with computational complexity analyses showing decidability under restrictions but NP-hardness generally.^[14]^[15] Subsequent developments include cumulative logics like System Z (Pearl, 1990), which order defaults by specificity for unique extensions, and skeptical versus credulous semantics distinguishing safe (intersection of extensions) from optimistic inferences. Negation-as-failure in logic programming, as in stable model semantics (Gelfond and Lifschitz, 1988), operationalizes non-monotonicity computationally: a stable model satisfies the program and its Gelfond-Lifschitz reduct, enabling answer-set programming for defeasible queries with well-founded semantics resolving loops. These systems underpin applications in knowledge representation, with formal proofs establishing soundness relative to Reiter extensions and empirical benchmarks demonstrating efficiency in domains like diagnosis, where adding evidence (e.g., symptoms) refines hypotheses without global recomputation.^[16]^[17]

Argumentation-Based Approaches

Argumentation-based approaches formalize defeasible reasoning by representing knowledge as a set of arguments—structured derivations supporting claims—and defining defeat or attack relations between conflicting arguments, enabling the selection of justified conclusions through dialectical processes rather than exhaustive proof enumeration.^[18] These methods inherently support non-monotonicity, as the addition of new arguments can introduce defeats that undermine previously justified ones without invalidating underlying proofs.^[18] Unlike syntactic rule application in defeasible logics, argumentation emphasizes justification via defense against counterarguments, drawing from philosophical models like Toulmin's layout but formalized computationally.^[19] The cornerstone is Phan Minh Dung's abstract argumentation framework (AF), proposed in 1995, defined as a pair ⟨AR, →⟩ where AR is a finite set of arguments and → ⊆ AR × AR is an attack relation indicating potential defeat.^[18] Acceptability semantics characterize justified argument sets (extensions) via conditions like conflict-freeness (no internal attacks) and admissibility (defense against external attackers).^[18] The grounded semantics, a unique minimal complete extension, is computed iteratively: start with unattacked arguments, then add those defended solely by previously included arguments, yielding skeptically accepted conclusions as those supported by grounded arguments.^[18] Other semantics, such as preferred (maximal admissible sets), allow multiple extensions to capture alternative resolutions in ambiguous cases.^[18] Defeasibility manifests in AFs because extensions are sensitive to the global conflict structure; an argument acceptable under one AF may become indefensible upon expansion with defeating evidence.^[18] To connect with defeasible logics, argumentation frameworks provide interpretive semantics; for example, Antoniou et al. (2000) and Governatori et al. (2004) map defeasible theories to Dung-style AFs where strict rules yield undefeated arguments, defeasible rules produce attackable ones, and defeaters generate pure attackers without defenses, ensuring the grounded extension aligns with the logic's procedural conclusions.^[20]^[21] This correspondence holds for both ambiguity-propagating and ambiguity-blocking variants of defeasible logic, with attacks modeling rule defeats.^[21] Structured instantiations like ASPIC+ (Modgil and Prakken, 2013) build arguments as inference trees from axiomatic premises, strict rules (monotonic inferences), and defeasible rules (jumping to conclusions unless defeated), with attacks classified as rebuttals (conflicting conclusions) or undercuts (negating rule applicability).^[19] Defeats are directed by rule priorities or premise status, often asymmetric to reflect evidential strength, and mapped to abstract AFs for semantics application.^[19] ASPIC+ supports preferences via valued extensions or meta-arguments, addressing limitations in pure abstract models by incorporating logical content, as used in legal reasoning where rule hierarchies prevent cycles.^[19]^[22] These frameworks enable computational implementation, with tools generating extensions for decision support under incomplete information.^[19]

Defeasible Logic Systems

Defeasible logic systems constitute a class of non-monotonic, rule-based formalisms specifically engineered to capture defeasible inference, where conclusions remain provisional and susceptible to revision upon new evidence. Pioneered by Donald Nute, these systems differentiate strict rules for unconditional entailment from defeasible rules that permit exceptions via rebutting or undercutting mechanisms, enabling efficient handling of incomplete or conflicting information without requiring full belief revision.^[23]^[24] The syntax employs a restricted first-order language with literals as basic units—either atomic formulas or their negations—and supports free variables in rules interpreted as schemata over ground terms. A theory comprises facts (ground literals serving as axioms), strict rules of form A_1, \dots, A_n \rightarrow p (consequent p follows definitively if antecedents hold), defeasible rules A_1, \dots, A_n \Rightarrow p (consequent supported tentatively absent defeat), and defeaters A_1, \dots, A_n ; \neg p (block p without affirming \neg p). Advanced variants incorporate an acyclic superiority relation > over rules with complementary literals to prioritize in conflicts, resolving ambiguities deterministically.^[25]^[26] Inference operates through a modular proof theory with tagged literals: +\Delta q (strict proof of q), -\Delta q (strict disproof), +\delta q (defeasible proof), and -\delta q (defeasible disproof). Four inference rules govern derivation: one for strict proof (applicable strict rules or facts), one for strict disproof (applicable strict rules to \neg q), one for defeasible proof (applicable defeasible rules without applicable defeaters or superior contrary rules), and one for defeasible disproof (applicable defeaters or superior opposing defeasible rules). Proofs proceed iteratively via a fixed-point characteristic function over rule applications, yielding polynomial-time decidability for propositional cases and soundness/completeness relative to a preferential semantics favoring undefeated arguments.^[25]^[27] These systems exhibit desirable properties such as modularity (inference independent of full theory closure) and skepticism (conclusions only if indefeasible across extensions), distinguishing them from credulous approaches in other non-monotonic logics. Implementations include Nute's d-Prolog (circa 1988-1992) for Prolog-based execution and extensions like Defeasible Logic Programming (DeLP), which integrates argumentation frameworks for handling rule priorities and warrants via dialectical analysis of pro/contra arguments.^[24]^[28] Ordered variants, emphasizing superiority for multi-agent or normative contexts, maintain computational tractability while modeling real-world priority-based defeat, as in legal rule application.^[29]

Historical Development

Philosophical Origins

The traditional philosophical emphasis on deductive validity as the sole criterion for sound reasoning dominated epistemology for much of Western thought, positing that conclusions are justified only if they follow necessarily from premises. This view, rooted in Aristotelian apodeictic syllogisms for scientific demonstration, began to erode in the twentieth century as philosophers recognized that everyday and empirical inferences—such as generalizations from observed patterns—often compel belief without deductive certainty and remain provisional pending further evidence. John L. Pollock highlighted this shift, contending that epistemic warrant arises from reason schemes like induction or analogy, which provide prima facie justification retractable upon encountering defeaters, such as conflicting observations or explanatory alternatives.^[1]^[30] A pivotal development occurred in post-Gettier epistemology, where defeasible reasoning formalized the vulnerability of justification to defeat. Edmund M. Gettier's 1963 analysis exposed cases in which seemingly justified true beliefs fail as knowledge due to unnoticed factors undermining the inference, prompting theories requiring the absence of defeaters for knowledge attribution. Philosophers like Peter D. Klein elaborated this into a defeasibility condition, distinguishing rebutting defeaters (evidence against the conclusion) from undercutting defeaters (evidence negating the inferential link), thereby framing justification as tentatively binding but revisable. This approach addressed how agents rationally maintain beliefs amid incomplete information, mirroring practical deliberation where initial warrants yield to superior reasons.^[31] The roots also intersect with Humean skepticism regarding induction, articulated in David Hume's 1748 Enquiry Concerning Human Understanding, where empirical predictions from past uniformities lack deductive grounding yet guide action unless contradicted by exceptions. Hume's demonstration that no logical necessity supports extrapolating observed constants to unobserved cases underscores the inherent tentativeness of such inferences, prefiguring defeasible structures by emphasizing their rational force alongside empirical fragility. This inductive paradigm influenced later defeasibilist accounts, as Pollock integrated it into non-deductive schemes defeated by empirical rebuttals or explanatory rivals, enabling modeling of belief revision in dynamic knowledge acquisition.^[1]

Emergence in AI and Computer Science

Defeasible reasoning gained traction in artificial intelligence and computer science in the late 1970s and early 1980s, driven by the recognition that classical monotonic logics could not adequately model commonsense reasoning, where conclusions drawn from incomplete information must often be provisional and revisable upon new evidence. Monotonic systems, by preserving all prior inferences when axioms are added, struggled with defaults, exceptions, and dynamic knowledge updates essential for practical AI applications like planning and natural language understanding.^[32] This shift was motivated by foundational issues in knowledge representation, including the frame problem—first explicitly identified by John McCarthy and Patrick J. Hayes in 1969—which demonstrated the inefficiency of specifying persistence or change for every unaffected fact in logical descriptions of actions.^[33] The formal emergence of non-monotonic logics, closely aligned with defeasible reasoning, crystallized in 1980 with Drew McDermott and Jon Doyle's "Non-Monotonic Logic I," which proposed inference relations allowing theorems to be invalidated by subsequent axioms, enabling more realistic simulation of human defeasible inference in computational systems.^[32] In the same year, Raymond Reiter's "A Logic for Default Reasoning" introduced default logic, a framework for applying generalized rules (e.g., "typically, birds fly") subject to consistency checks against exceptions, addressing qualification problems where exhaustive conditions cannot be enumerated.^[12] These innovations marked a departure from deductive purity toward hybrid systems integrating strict and defeasible rules, influencing subsequent AI research on belief revision and argumentation. John L. Pollock further operationalized defeasible reasoning computationally during the 1980s, arguing in his 1987 Cognitive Science paper that such reasoning underpins justification in non-deductive contexts and implementing it via rule-based engines like the OSCAR system for automated reasoning under uncertainty.^[2] Defeasible logic, a streamlined non-monotonic variant emphasizing skeptical conclusions from conflicting rules, was systematized by Donald Nute in works from the early 1990s, including basic formulations extensible to Prolog-like implementations for efficient decision support.^[26] These advancements facilitated defeasible approaches in broader computer science domains, such as expert systems and legal reasoning models, by prioritizing computational tractability over completeness.^[24]

Key Milestones and Theorists

The formal study of defeasible reasoning gained momentum in 1980 with the simultaneous publication of foundational works on non-monotonic logics in Artificial Intelligence. Drew McDermott and Jon Doyle's "Non-Monotonic Logic I" presented a model-theoretic semantics and proof procedure for consequence relations that permit the retraction of conclusions upon new evidence, addressing the limitations of monotonic deduction in commonsense reasoning.^[32] In the same year, Raymond Reiter introduced default logic, which formalizes defaults as rules with prerequisites, justifications, and consequences, generating belief sets (extensions) that can accommodate exceptions without global revision.^[10] John McCarthy's circumscription principle complemented these by minimizing the extension of abnormality predicates to support inductive defaults, such as "all birds fly unless abnormal." Subsequent developments refined these frameworks for computational tractability and epistemological rigor. In the mid-1980s, John L. Pollock advanced a warrant-based theory of defeasible reasoning, distinguishing prima facie reasons from ultimate warrants and incorporating rebutting and undercutting defeaters to model justification degrees in autonomous agents; this underpinned his OSCAR system, first prototyped around 1986 and iteratively developed through the 1990s.^[34] Dov Gabbay's 1985 analysis established key semantic properties like cautious monotonicity and cut for non-monotonic entailment, influencing subsequent preferential logics. By the early 1990s, specialized systems emerged, including Donald Nute's defeasible logic (initially detailed in 1991-1992 works and formalized in a 1994 paper), a skeptical, rule-based non-monotonic system using strict rules, defeasible rules, and defeat conditions to compute conclusions without floating conclusions or cycles.^[35] Later extensions, such as Grigoris Antoniou's prioritized defeasible logic in the late 1990s, incorporated superiority relations among rules to resolve conflicts, enhancing applicability in knowledge representation. These milestones collectively shifted defeasible reasoning from philosophical intuition to computable frameworks, enabling AI systems to handle uncertainty and defaults empirically.

Applications and Practical Uses

Legal and Judicial Contexts

Defeasible reasoning permeates legal and judicial processes, where conclusions drawn from statutes, precedents, or evidence are provisional and subject to revision upon the introduction of overriding factors. General legal rules, such as those establishing presumptions or default obligations, often yield to exceptions defined by higher norms, specific facts, or countervailing evidence, mirroring the non-monotonic nature of defeasible inference. For instance, in criminal proceedings, the presumption of innocence holds until defeated by proof beyond a reasonable doubt, allowing new testimony or forensic data to retract initial assessments of guilt.^[7]^[36] This structure accommodates the inherent incompleteness of legal codes, which cannot enumerate all scenarios exhaustively, necessitating inference that permits defeat without invalidating the underlying rule.^[37] Formal models of defeasible reasoning, including defeasible logic and argumentation frameworks, have been adapted to capture these dynamics in legal norm representation. Defeasible logic systems encode statutes as rules with defeaters—conditions that block conclusions without negating the rule itself—facilitating the modeling of hierarchies where superior norms prevail, as in constitutional overrides of legislation.^[38] Argumentation-based approaches, such as those using abstract argumentation semantics, simulate judicial debates by treating arguments as nodes that attack or defeat one another based on preferences derived from legal principles like proportionality or equity.^[38] Empirical studies demonstrate that individuals process legal conditionals defeasibly, generating valid but overridable inferences (e.g., "if A then ordinarily B") that align with how courts interpret clauses susceptible to exceptions, such as implied terms in contracts defeated by express agreements.^[39] In practical judicial applications, defeasible reasoning underpins case-based reasoning and precedent analysis, where prior rulings serve as defaults rebuttable by distinguishing facts or evolving societal norms. Appellate courts exemplify this by overturning lower decisions when new evidence emerges, as seen in systems integrating non-monotonic elements to handle burdens of proof shifts.^[40] Recent computational efforts automate these processes in rule-based legal expert systems, applying defeasible inference to contracts and regulatory compliance, where automated checks flag potential defeaters like force majeure clauses.^[41] Such tools, evaluated on benchmarks of legal norms, enhance efficiency in domains like dispute resolution but require careful calibration to avoid under-defeating robust rules.^[41] Overall, these applications underscore defeasible reasoning's utility in maintaining legal adaptability amid uncertainty, though implementations must prioritize verifiable rule priorities to ensure consistency.^[42]

Artificial Intelligence and Commonsense Reasoning

Defeasible reasoning underpins artificial intelligence efforts to model commonsense knowledge representation, enabling systems to apply default rules—such as presuming that birds fly or doors open—while allowing exceptions to override these assumptions based on additional evidence.^[43] This non-monotonic approach addresses the limitations of classical deductive logic, which cannot retract conclusions or handle incomplete world knowledge prevalent in everyday scenarios.^[10] Pioneering formalisms include John McCarthy's circumscription, introduced in 1980, which minimizes the scope of predicates like "abnormal" to formalize commonsense assumptions that entities behave as expected unless proven otherwise, facilitating inferences in AI planning and prediction tasks.^[14] Similarly, Raymond Reiter's default logic, proposed in 1980, extends first-order logic with rules permitting conclusions from premises and consistent justifications, such as deriving "Tweety flies" from "Tweety is a bird" via a default unless contradicted by evidence of abnormality.^[44] These mechanisms have been foundational for constructing knowledge bases that support provisional reasoning in uncertain environments. Large-scale projects like Cyc, initiated in 1984, encode millions of defeasible assertions—comprising about 95% of its knowledge base—to capture nuanced commonsense relations, including temporal persistence and contextual exceptions, enabling inference engines to generate explanations and handle real-world variability.^[45] In practice, such systems apply defeasible reasoning to domains like robotics and diagnostic AI, where defaults guide actions (e.g., assuming an object is graspable) subject to sensory overrides. In natural language processing, defeasible reasoning manifests in tasks requiring models to evaluate entailments that hold provisionally, as in defeasible natural language inference datasets where premises like "A soccer game with multiple males is happening" defeasibly support "Some people are playing a sport" but fail under exceptions like weather interruptions.^[46] Benchmarks such as DEFREASING, introduced in 2025, assess large language models' handling of generic property inheritance (e.g., "Robots are machines, so they have circuits" defeased by specifics), revealing persistent gaps in overturning defaults consistently compared to human performance.^[47] Neuro-symbolic architectures increasingly fuse defeasible logics with machine learning to bolster commonsense capabilities, embedding non-monotonic rules within neural networks for hybrid inference that learns exceptions from data while preserving logical rigor, as seen in frameworks for decision-making under uncertainty.^[48] Despite advances, computational intractability in scaling these methods to vast knowledge graphs remains a barrier, limiting deployment in real-time AI applications.^[49]

Diagnostic and Decision-Making Domains

Defeasible reasoning underpins diagnostic processes in medicine by enabling the integration of incomplete and evolving evidence, where initial inferences from symptoms or sensors can be overridden by confirmatory or contradictory data. In healthcare systems, multi-agent formalisms based on contextual defeasible logic manage inconsistencies across distributed data sources, such as smart home sensors and hospital records, to support real-time monitoring of conditions like Parkinson's disease. A 2022 framework employs strict, defeasible, and defeater rules with preference orderings to resolve conflicts post-data acquisition, prioritizing agents focused on vital signs like heart rate and motion in simulations using NetLogo. For instance, in a case study involving a patient named Alice, initial defeasible conclusions from elevated CO levels and reduced mobility trigger alerts, which may be revised if subsequent sensor inputs indicate benign fluctuations.^[50] Argumentation-based systems further apply defeasible reasoning in clinical decision support, translating expert knowledge into structured arguments to handle uncertainty in oncology. These systems predict breast cancer recurrence after surgery by weighing partial evidence against potential exceptions and facilitate group deliberations among oncologists for head-and-neck cancer treatments or organ transplant viability assessments. A 2014 study highlights their utility in genetic counseling simulations, where eight counselors evaluated cancer risk mutations, demonstrating improved handling of dynamic evidence over traditional methods. In non-medical diagnostics, such as agricultural expert systems, non-monotonic variants revise hypotheses iteratively; a 2002 Prolog-based tool for cucumber disorder identification shifts from nutrient deficiency inferences to root knot nematodes upon observing wilted leaves and root anomalies, maintaining consistency amid incomplete field data.^[51]^[52] In decision-making domains, defeasible reasoning supports multi-agent coordination by combining knowledge bases while detecting and mitigating conflicts, essential for scenarios with ambiguous or evolving information. The DAMN platform, introduced at AAAI in 2020, visualizes contradictions via statement graphs, allowing agents to reason over merged rules with semantics for ambiguity blocking or propagation, as in a European wine waste management case where an obsolete law claim conflicted with updated regulations from the EU H2020 NoAW project (2016–2020). This enables defeasible conclusions, such as resolving whether a penguin can fly based on default bird assumptions defeated by species-specific facts. Earlier systems like EVID, developed in the early 1990s, provide interactive defeasible inference for belief revision in decision support, inferring provisional outcomes from clausal knowledge that retract upon defeating evidence. Such approaches enhance causal realism in decisions by privileging empirical overrides, as exemplified in Pollock's analysis where medical symptoms offer a 0.6 probabilistic defeasible justification for lactose intolerance, scalable to degrees of evidential strength.^[53]^[54]^[55]

Criticisms and Limitations

Formal and Computational Challenges

One formal challenge in defeasible reasoning arises from precisely defining defeat relations between conflicting rules or arguments, as different criteria such as specificity, recency, or explicit priorities can lead to divergent outcomes, complicating the establishment of a unique semantics. For instance, in systems incorporating specificity, determining whether a more specific rule overrides a general one requires resolving ambiguities in rule applicability, which can result in reinstatement issues where defeated conclusions are later revived under updated evidence.^[56] This lack of consensus on defeat mechanisms often necessitates ad hoc extensions to base logics, undermining formal elegance and interoperability across systems.^[57] Another formal difficulty involves integrating defeasible rules with monotonic components, such as strict implications, while preserving desirable properties like soundness and completeness; extensions for preferences or deontic modalities frequently introduce inconsistencies or lose decidability in expressive fragments.^[58] Epistemic challenges further emerge in modeling belief revision under defeasibility, where fixed-point constructions for stable extensions demand careful handling of circular justifications, potentially yielding multiple incompatible conclusions.^[57] Computationally, while propositional defeasible logic achieves linear-time inference via forward-chaining procedures, this efficiency relies on simple defeat without priorities, and incorporating superiorities or dynamic rules elevates complexity to polynomial or higher degrees in extended variants.^[59] ^[60] In broader non-monotonic frameworks underpinning defeasible reasoning, such as default logic, credulous entailment—determining if a conclusion holds in some extension—is Σ₂ᵖ-complete, reflecting the inherent nondeterminism of selecting consistent defaults amid exceptions.^[57] Skeptical entailment, requiring consistency across all extensions, reaches Π₂ᵖ-completeness, posing scalability barriers for knowledge bases with thousands of rules, as argument construction and status evaluation become intractable.^[57] ^[61] These complexities amplify in dialectical systems like defeasible logic programming, where warrant computation via proponent-opponent debates incurs additional overhead from exhaustive case analysis.^[62]

Epistemological and Philosophical Critiques

Epistemological critiques of defeasible reasoning emphasize its inherent vulnerability to rebuttal by new evidence, which challenges its adequacy for yielding stable justifications sufficient for knowledge. In traditional epistemology, knowledge often demands indefeasible warrant, where beliefs resist defeat; defeasible inferences, by contrast, provisionally endorse conclusions that may retract upon further information, potentially rendering no belief epistemically secure in the face of endless possible defeaters. This aligns with analyses of defeaters as liabilities that undermine positive epistemic status, such as justification or entitlement, without providing a mechanism to terminate justificatory regress.^[8] Philosophers have objected that formalizations of defeasible reasoning, particularly via non-monotonic logics, conflate proof-theoretic operations with epistemic processes of belief revision. McDermott and Doyle argue that non-monotonic systems misapply deductive rules like modus ponens as policies for rational belief, ignoring how evidence against a conclusion should override syntactic validity; for instance, consistency operators (e.g., ensuring premises cohere) distort simple assumptions into complex semantic constraints, complicating rather than clarifying representation.^[63] Such approaches, they contend, fail to address belief fixation through epistemological means, instead borrowing from logic in ways that obscure the distinction between valid inference and warranted acceptance.^[63] Further philosophical concerns arise from the undecidability inherent in non-monotonic frameworks, paralleling Church's theorem on the limits of effective procedures, which compels reliance on non-algorithmic heuristics rather than precise consequence relations. This undermines the aspiration of defeasible reasoning to model commonsense rationality with logical rigor, as multiple belief extensions can emerge from the same premises, yielding ambiguous or context-dependent outcomes without principled resolution. Critics thus view these systems as heuristic approximations rather than foundational tools for epistemic normativity, better suited to descriptive psychology than prescriptive philosophy.^[63]

Risks of Over-Reliance in Practice

Over-reliance on defeasible reasoning in practical settings can result in the acceptance of provisional conclusions as definitive, particularly when new or defeating evidence emerges after initial inferences are acted upon. Unlike deductive reasoning, which guarantees validity from premises, defeasible processes yield rationally compelling but revisable outcomes, increasing vulnerability to errors in dynamic environments where information incompleteness is common. For instance, in diagnostic domains, default assumptions about symptom-probability links may lead clinicians to overlook rare but critical exceptions, as seen in cases where probabilistic defaults in medical AI systems have contributed to delayed interventions for atypical presentations.^[64] In legal and judicial applications, presumptions grounded in defeasible rules—such as burdens of proof or statutory defaults—risk miscarriages of justice if rebuttals are insufficiently scrutinized or if systemic biases influence the weighting of exceptions. Legal scholars note that the inherent defeasibility of normative arguments, where rules compete with countervailing factors, can amplify overconfidence in prima facie cases, potentially leading to erroneous convictions or acquittals when exceptional circumstances are downplayed. This issue is exacerbated in high-volume adjudication, where time constraints discourage exhaustive defeat-testing, as evidenced by critiques of presumption-heavy frameworks in civil law traditions.^[38]^[36] Artificial intelligence systems employing defeasible mechanisms, such as argumentation frameworks in large language models for legal reasoning, face heightened risks of hallucination and flawed outputs due to incomplete knowledge bases or improper exception handling. Empirical evaluations of LLMs in judicial tasks reveal that over-reliance on defeasible plausibility assessments—balancing facts against defeasible theories—often produces inconsistent verdicts when factual inputs are ambiguous, underscoring the need for human oversight to mitigate propagation of unverified defaults. In high-stakes AI-driven decisions, such as autonomous risk assessment, failure to robustly model defeaters has led to documented errors, including biased prioritizations in resource allocation under uncertainty.^[65]^[66] Broader decision-making contexts, including policy formulation, highlight how defeasible reasoning's tolerance for exceptions can foster policy reversals or inefficiencies when initial defaults prove brittle against evolving data. For example, public health guidelines relying on defeasible epidemiological inferences during crises, like early COVID-19 mask recommendations based on default asymptomatic transmission assumptions, were later overridden, resulting in public confusion and suboptimal compliance. Over-reliance without rigorous defeat-validation protocols thus undermines causal accountability, as provisional policies may entrench path dependencies that resist correction even after evidentiary shifts.^[67]

Recent Developments

Advances in Formal Models (2020-2025)

In 2023, researchers advanced dynamic aspects of structured argumentation formalisms by introducing conclusion-vulnerability abstract frameworks (cvAFs), which extend abstract argumentation to handle knowledge base changes in assumption-based argumentation (ABA) frameworks central to defeasible reasoning. This model addresses intractability in enforcement of target atoms and strong equivalence by providing syntactic conditions for tractability, establishing a correspondence between ABA dynamics and cvAFs that enables efficient computation in restricted fragments.^[68] A 2024 publication formalized nonmonotonic reasoning with defeasible rules across feasible and infeasible worlds, emphasizing inductive inference operators that map conditional belief bases to inference relations while distinguishing weak and strong consistency. The framework proves properties of inferences from weakly consistent bases and evaluates inference methods against postulates for nonmonotonic reasoning, offering a rigorous basis for handling defeasible conditionals in belief revision.^[69] In normative contexts, a proof-theoretic approach integrated defeasible argumentation with constrained input/output (I/O) logics in 2024, using annotated sequent calculi to track defeasible status and resolve norm conflicts through maximally consistent premise sets. This links annotated proofs to grounded extensions in logical argumentation frameworks, providing a transparent computational model for nonmonotonic normative reasoning.^[70] Extensions to defeasible logic for revision appeared in 2024, proposing operators that minimize rule removal by identifying necessary and sufficient rules to prove claims in non-monotonic theories, thereby preserving as much of the original knowledge base as possible during updates.^[56] Further formalization in 2025 combined defeasible rules with geographic knowledge graphs (GeoKGs), employing priority-based conflict resolution in ontologies to infer contextual similarities, such as for geospatial entities, enhancing interpretability over embedding-based methods.^[71]

Integration with Machine Learning

Neurosymbolic AI frameworks have incorporated defeasible reasoning by hybridizing neural networks for perceptual tasks with symbolic components for non-monotonic inference, enabling models to revise conclusions based on new evidence. A key approach, Continual Reasoning, integrates Logic Tensor Networks with continual learning techniques, using a curriculum that alternates between knowledge assimilation and data-driven recall to handle retraction of prior beliefs. This method achieves superior accuracy on prototypical non-monotonic problems compared to standard symbolic or neural baselines, addressing the limitations of monotonic deep learning in commonsense scenarios.^[72] In natural language processing, defeasible reasoning is integrated via benchmarks that extend natural language inference (NLI) to defeasible cases, training models to predict plausible but revisable entailments. The DEFREASING dataset, comprising approximately 95,000 questions derived from generics semantics and inheritance rules, evaluates large language models (LLMs) across five patterns including strengthening, weakening, and neutral impacts. Assessments of 12 instruction-tuned LLMs, such as Llama3 and Mixtral, yield maximum F1 scores of about 0.64, with consistent underperformance in weakening inferences and sensitivity to irrelevant new information, underscoring the need for targeted fine-tuning.^[47] Multimodal extensions further embed defeasible reasoning in vision-language models through tasks like Defeasible Visual Entailment (DVE), where entailment relations between images and text can be altered by supplementary evidence. This benchmark promotes reward-driven optimization in models, facilitating defeasible updates in visual reasoning pipelines.^[73] Datasets translating formal defeasible rules into textual prompts have also enabled LLMs to approximate defeasible logic patterns, though initial experiments reveal gaps in handling complex defeaters without explicit symbolic grounding.^[74]

References

[1]
[PDF] Defeasible Reasoning - John Horty
By definition, defeasible reasoning is synchronically defeasible, in the sense that the addition of new in- formation (new initial nodes) can lead ...
[2]
Defeasible Reasoning - Pollock - 1987 - Wiley Online Library
What philosophers call defeasible reasoning is roughly the same as nonmonotonic reasoning in AI. Some brief remarks are made about the nature of reasoning.
[3]
[PDF] arXiv:cs/0003013v1 [cs.AI] 7 Mar 2000
The family of defeasible logics was introduced by Nute. We begin by outlining the constructs in defeasible logics. We then define the inference rules of a ...
[4]
[PDF] Defeasible Reasoning - John Horty
It is prima facie reasons and defeaters that are responsible for the nonmono- tonic character of human reasoning. There are two kinds of defeaters for prima ...
[5]
[PDF] A theory of defeasible reasoning - John Horty
In philosophy, this is described by saying that reasoning is defeasible. In AI, it is described by saying that reasoning is nonmonotonic. My ultimate objective ...
[6]
Defeasible reasoning - ScienceDirect.com
Defeasible reasoning. Author links open overlay panel. John L. Pollock. Show ... McDermott D., Doyle J. Non-monotonic logic I. Artificial Intelligence, 13 ...
[7]
Defeasible Reasoning - Stanford Encyclopedia of Philosophy
Jan 21, 2005 · Reasoning is defeasible when the corresponding argument is rationally compelling but not deductively valid. The truth of the premises of a ...History · Epistemological Approaches · Logical Approaches
[8]
Defeaters in Epistemology | Internet Encyclopedia of Philosophy
Defeasibility refers to a kind of epistemic liability or vulnerability, the potential of loss, reduction, or prevention of some positive epistemic status.
[9]
[PDF] non-monotonic logic i - DSpace@MIT
This paper studies the foundations of these forms of reasoning with revisions which we term non- monotonic logic. Traditional logics are called monotonic ...
[10]
[PDF] NONMONOTONIC REASONING - Cornell: Computer Science
Reasoning with incomplete information: investigations of non-monotonic reasoning. PhD thesis. Univ. British Columbia, Vancouver. 151 pp. Etherington, D. W. 1987 ...
[11]
[PDF] FOUNDATIONS OF N0N-MONOTONIC REASONING - mimuw
Definition 1. By non-monotonic reasoning we understand the drawing of conclusions which may be invalidated in the light of new information. A logical system ...
[12]
A logic for default reasoning - ScienceDirect.com
In this paper we propose a logic for default reasoning. We then specialize our treatment to a very large class of commonly occuring defaults.
[13]
[PDF] ON INTERACTING DEFAULTS Raymond Reiter Department ... - IJCAI
In an earlier paper [Reiter 1980a] one of us proposed a logic for default reasoning. The objec- tive there was to provide a representation for, among other ...
[14]
[PDF] CIRCUMSCRIPTION—A FORM OF NONMONOTONIC REASONING
Circumscription formalizes such conjectural reasoning. 1 INTRODUCTION. THE QUALIFICATION. PROBLEM. (McCarthy 1959)1 proposed a program with “common sense” that ...
[15]
Circumscription—A form of non-monotonic reasoning - ScienceDirect
McCarthy. Programs with common sense. Proceedings of the Teddington Conference on the Mechanization of Thought Processes, H.M. Stationery Office, London (1960).
[16]
A logical framework for default reasoning - ScienceDirect.com
By treating defaults as predefined possible hypotheses we show how this idea subsumes the intuition behind Reiter's default logic. Solutions to multiple ...<|separator|>
[17]
Some results on default logic | Journal of Computer Science and ...
And a class of defaults, so-called Auto-compatible Default Theory, is also introduced. All these essentially develop the theories of Reiter and his followers.
[18]
On the acceptability of arguments and its fundamental role in ...
Aravindan and P.M. Dung, Partial deduction of logic programs with respect to well-founded semantics, New Generation Comput. (to appear). Google Scholar. C ...
[19]
The ASPIC+ framework for structured argumentation: a tutorial
This article gives a tutorial introduction to the ASPIC+ framework for structured argumentation. The philosophical and conceptual underpinnings of ASPIC+ ...
[20]
[PDF] Argumentation Semantics for Defeasible Logics - UQ eSpace
Dung [9,10] proposed an abstract argumentation framework giving rise to several argu- mentation semantics, in particular to a skeptical semantics (called ...<|separator|>
[21]
Argumentation Semantics for Defeasible Logic - Oxford Academic
In this paper we will adapt Dung's framework to provide argumentation semantics for the two defeasible logics we investigate. We show that Dung's grounded ...
[22]
On ASPIC and Defeasible Logic - IOS Press Ebooks
Abstract. Dung-like argumentation framework ASPIC+ and Defeasible Logic (DL) are both well-studied rule-based formalisms for defeasible reasoning.
[23]
[PDF] Defeasible Logic - ResearchGate
Defeasible logic uses strict rules, defeasible rules, and undercutting defeaters. We define atomic formulas in the usual way. A literal is any atomic formula or ...
[24]
[PDF] Defeasible Prolog Donald Nute
Defeasible logic uses strict rules, defeasible rules, and undercutting defeaters. 1 The basics. We begin by defining the language of d-Prolog. One unary functor ...
[25]
[PDF] Defeasible Logic - mimuw
Formal definition. Language. A fixed subset of first-order language containing a finite set of constants and a finite set of relations. Both facts and rules ...
[26]
Defeasible Logic | SpringerLink
Mar 14, 2003 · Nute, D.: Defeasible logic. In Gabbay, D., Hogger, C., eds.: Handbook of Logic for Artificial Intelligence and Logic Programming. Volume III ...
[27]
Representation results for defeasible logic - ACM Digital Library
This paper investigates transformations and normal forms in the context of Defeasible Logic, a simple but efficient formalism for nonmonotonic reasoning.<|separator|>
[28]
[PDF] Defeasible logic programming: an argumentative approach - SciSpace
The work reported here introduces Defeasible Logic Programming (DeLP), a formalism that combines results of Logic Programming and Defeasible Argumentation.
[29]
Ordered logic: defeasible reasoning for multiple agents
We present a generalized proof theory for defeasible reasoning and briefly explain the relationship of this system to other nonmonotonic formalisms.
[30]
John L. Pollock, Defeasible Reasoning - PhilPapers
There was a long tradition in philosophy according to which good reasoning had to be deductively valid. However, that tradition began to be questioned in ...
[31]
[PDF] Defeasibility in Epistemology - Aleks Knoks -
In any event, it tackles epistemological questions drawing on a completely different formal framework, namely, that of logics for defeasible reasoning. This ...
[32]
Non-monotonic logic I - ScienceDirect.com
'Non-monotonic' logical systems are logics in which the introduction of new axioms can invalidate old theorems.
[33]
Frame Problem - an overview | ScienceDirect Topics
The frame problem was first explicitly identified by John McCarthy and Patrick Hayes in 1969 during efforts to design artificially intelligent machines capable ...
[34]
[PDF] Defeasible Reasoning in OSCAR - John L. Pollock
The objective of the OSCAR project is the construction of a general theory of rationality for autonomous agents and its im- plementation in an AI system. OSCAR ...
[35]
Defeasible Reasoning - Flora-2
Defeasible Logic was introduced by Donald Nute in a 1994 seminal paper. The basic idea behind defeasible reasoning is that logic rules may contradict each ...
[36]
Defeasible reasoning in law - ScienceDirect.com
An Introduction to Law and Legal Reasoning. Little, Brown and Company (1985) ... A Non-monotonic Logic Based on Conditional Logic. Advanced Computational ...
[37]
Defeasibility in Legal Reasoning - Oxford Academic
Definition C.2 Defeasible reasoning schema. A reasoning schema is defeasible if one should, under certain conditions, refrain from adopting its conclusions ...
[38]
Argumentation and Defeasible Reasoning in the Law - MDPI
Dec 18, 2021 · In this work, we provide an overview of the following logic-based approaches to defeasible reasoning: defeasible logic, Answer Set Programming, ABA+, ASPIC+, ...<|separator|>
[39]
Defeasible reasoning with legal conditionals | Memory & Cognition
Dec 21, 2015 · Valid conclusions can be defeated if people can think of conditions that prevent the consequent to occur although the antecedent is given.
[40]
(PDF) Defeasibility in Legal Reasoning - ResearchGate
Jan 15, 2015 · It analyzes the process of defeasible reasoning considering collisions of reasons, defeat, preferencebased reasoning and reinstatement, as well ...
[41]
[2205.07335] Automating Defeasible Reasoning in Law - arXiv
May 15, 2022 · The paper studies defeasible reasoning in rule-based systems, in particular about legal norms and contracts.
[42]
Legal Defeasibility in Context and the Emergence of Substantial ...
May 24, 2014 · An inference drawing a conclusion is defeasible if there is some other proposition that, if taken in conjunction with the original proposition, ...
[43]
Nonmonotonic Reasoning: Logical Foundations of Commonsense ...
Nonmonotonic reasoning forms one of the main components of the logical approach to Artificial Intelligence and Knowledge Representation.
[44]
[PDF] A Logic for Default Reasoning - John Horty
In general the relationship between default and non-monotonic logics appears to be complex. A few results relating the two will be contained in a forthcoming.
[45]
[PDF] Cyc - AAAI Publications
Third, it should provide some scheme for expressing and reasoning with default knowl- edge. Almost all the knowledge in Cyc is defeasible. Only about 5 percent ...
[46]
Thinking Like a Skeptic: Defeasible Inference in Natural Language
Defeasible inference is reasoning where an inference can be weakened or overturned by new evidence, like 'X is a bird, therefore X flies' but 'X is a penguin'.
[47]
[PDF] Evaluating Defeasible Reasoning in LLMs with DEFREASING
Apr 29, 2025 · A defeasible inference is a plausible conclusion ... In Proceedings of the Second international Workshop on Non-monotonic Reason- ing, pages 202– ...
[48]
Integrating Non-monotonic Logical Reasoning and Inductive ...
Between these deep networks, it embeds components for non-monotonic logical reasoning with incomplete commonsense domain knowledge, and for decision tree ...
[49]
A survey of non-monotonic reasoning
This paper discusses two of the most prominent formalizations of common-sense reasoning. No prior knowledge of formal logic is required.
[50]
A Multi-Agent Formalism Based on Contextual Defeasible Logic for ...
Mar 3, 2022 · Contextual defeasible reasoning (CDL) is applied to the system after the information flow and used to handle any inconsistencies.
[51]
[PDF] Defeasible Reasoning and Argument-Based Systems in Medical ...
Defeasible reasoning has increasingly gained attention in the medical sector because it supports reasoning over partial, incomplete and dynamic evidence and ...
[52]
Diagnostic expert system using non-monotonic reasoning
Non-monotonic reasoning means that our intermediate beliefs may be changed according to additional information. Eitherington, Kraus, and Perlis (1991) define ...
[53]
[PDF] DAMN: Defeasible Reasoning Tool for Multi-Agent Reasoning
Abstract. This demonstration paper introduces DAMN: a defeasible reasoning platform available on the web. It is geared towards.
[54]
[PDF] EVID: A System for Interactive Defeasible Reasoning
Yet, the definite/defeasible distinction is not an absolute one, but is relative to the context of an application and determined by the designer of the KB ...
[55]
[PDF] Defeasible Reasoning and Degrees of Justification - John L. Pollock
see Pollock 2008, 2008a, ...
[56]
Revising non-monotonic theories with sufficient and necessary ...
Sep 6, 2024 · For non-monotonic defeasible reasoning, things are more complicated. First of all, we cannot decide what to remove or substitute, based on ...
[57]
https://arxiv.org/pdf/1009.1990.pdf
[58]
[PDF] A Preference-Based Approach to Defeasible Deontic Inference
While there are computational challenges, these are no worse than those of ... Defeasible logic. In Gabbay, D. M.; Hog- ger, C. J.; and Robinson, J. A. ...
[59]
Propositional defeasible logic has linear complexity
Defeasible logic has linear complexity. 709. A significant non-factor in the complexity of defeasible logic is the team defeat aspect of the logic. This might ...
[60]
https://academic.oup.com/logcom/advance-article/doi/10.1093/logcom/exae029/7717782
[61]
Proof-complexity results for nonmonotonic reasoning
Abstract. It is well-known that almost all nonmonotonic formalisms have a higher worst-case complexity than classical reasoning.
[62]
[PDF] An Analysis of the Computational Complexity of DeLP through ...
Defeasible Logic Programming (DeLP) is a suitable tool for knowledge representation and reasoning. Its operational semantics is based on a dialectical analysis ...
[63]
[PDF] 1980 - What's Wrong with Non-Monotonic Logic?
logic. - in virtue of its monotoniqity. - is incapable of adequately capturing or representing certain crucial features.
[64]
Think about it! Improving defeasible reasoning by first modeling the ...
Generally, high-stakes applications or applications dependent on specific linguistic or specialized domain knowledge may need to rely on feedback from human ...
[65]
LLMs for legal reasoning: A unified framework and future perspectives
The framework evaluates the plausibility of the outcome from fact and theory. Since theory is defeasible and factual description alone are often incomplete or ...
[66]
AI under great uncertainty: implications and decision strategies for ...
Sep 7, 2021 · This paper argues that public policy decisions on how and if to implement decision-making processes based on machine learning and AI for public ...
[67]
The Dual Aspects of Legal Reasoning in the Era of Artificial ...
Defeasible reasoning can only provide “guarantee” at best and cannot provide “confirmation” like deductive reasoning.
[68]
https://www.jair.org/index.php/jair/article/view/14481
[69]
https://www.iospress.com/node15242
[70]
Defeasible Normative Reasoning: A Proof-Theoretic Integration of ...
Mar 24, 2024 · Defeasible normative reasoning uses nonmonotonic reasoning to resolve conflicts among norms, extending proof systems with annotations to track ...
[71]
[PDF] GeoKG-Enabled Similarity Computation with Defeasible Reasoning
Apr 10, 2025 · Finally, the sheer scale of geospatial datasets poses computational challenges ... defeasible logic to solve real-world geospatial problems. For ...
[72]
Non-Monotonic Reasoning in Neurosymbolic AI using Continual ...
May 3, 2023 · Non-monotonicity is a property of non-classical reasoning typically seen in commonsense reasoning, whereby a reasoning system is allowed ( ...
[73]
Defeasible Visual Entailment: Benchmark, Evaluator, and Reward ...
Dec 19, 2024 · We introduce a new task called Defeasible Visual Entailment (DVE), where the goal is to allow the modification of the entailment relationship.
[74]
[2410.12509] Benchmarking Defeasible Reasoning with Large Language Models -- Initial Experiments and Future Directions
No readable text found in the HTML.<|separator|>