Plausible reasoning for the problems of cognitive sociology

The plausible reasoning class (called the JSM-reasoning in honour of John Stuart Mill) is described. It implements interaction of three forms of non-deductive procedures induction, analogy and abduction. Empirical induction in the JSM-reasoning is the basis for generation of hypotheses on causal relations (determinants of social behaviour). Inference by analogy means that predictions about previously unknown properties of objects (individual’s behaviour) are inferred from causal relations. Abductive inference is performed to check on the explanatory adequacy of generated hypotheses. To recognize rationality of respondents’ opinion deductive inference is used. Plausible reasoning, semantics of argumentation logic and deductive recognition of opinion rationality represent logical tool for cognitive sociology problems

Does Suppositional Reasoning Solve the Bootstrapping Problem?

In a 2002 article Stewart Cohen advances the “bootstrapping problem” for what he calls “basic justification theories,” and in a 2010 followup he offers a solution to the problem, exploiting the idea that suppositional reasoning may be used with defeasible as well as with deductive inference rules. To curtail the form of bootstrapping permitted by basic justification theories, Cohen insists that subjects must know their perceptual faculties are reliable before perception can give them knowledge. But how is such knowledge of reliability to be acquired if not through perception itself? Cohen proposes that such knowledge may be acquired a priori through suppositional reasoning. I argue that his strategy runs afoul of a plausible view about how epistemic principles function; in brief, I argue that one must actually satisfy the antecedent of an epistemic principle, not merely suppose that one does, to acquire any justification by its means – even justification for a merely conditional proposition

Defeasible disjunctive datalog

Datalog is a declarative logic programming language that uses classical logical reasoning as its basic form of reasoning. Defeasible reasoning is a form of non-classical reasoning that is able to deal with exceptions to general assertions in a formal manner. The KLM approach to defeasible reasoning is an axiomatic approach based on the concept of plausible inference. Since Datalog uses classical reasoning, it is currently not able to handle defeasible implications and exceptions. We aim to extend the expressivity of Datalog by incorporating KLM-style defeasi- ble reasoning into classical Datalog. We present a systematic approach to extending the KLM properties and a well-known form of defeasible entailment: Rational Closure. We conclude by exploring Datalog exten- sions of less conservative forms of defeasible entailment: Relevant and Lexicographic Closure

Plausible inference: A multi-valued logic for problem solving

A new logic is developed which permits continuously variable strength of belief in the truth of assertions. Four inference rules result, with formal logic as a limiting case. Quantification of belief is defined. Propagation of belief to linked assertions results from dependency-based techniques of truth maintenance so that local consistency is achieved or contradiction discovered in problem solving. Rules for combining, confirming, or disconfirming beliefs are given, and several heuristics are suggested that apply to revising already formed beliefs in the light of new evidence. The strength of belief that results in such revisions based on conflicting evidence are a highly subjective phenomenon. Certain quantification rules appear to reflect an orderliness in the subjectivity. Several examples of reasoning by plausible inference are given, including a legal example and one from robot learning. Propagation of belief takes place in directions forbidden in formal logic and this results in conclusions becoming possible for a given set of assertions that are not reachable by formal logic

Approaches to abductive reasoning : an overview

Abduction is a form of non-monotonic reasoning that has gained increasing interest in the last few years. The key idea behind it can be represented by the following inference rule frac{varphirightarrowomega,}{varphi}omega, i.e., from an occurrence of omega and the rule "varphi implies omega';, infer an occurrence of varphi as a plausible hypothesis or explanation for omega. Thus, in contrast to deduction, abduction is as well as induction a form of "defeasible'; inference, i.e., the formulae sanctioned are plausible and submitted to verification. In this paper, a formal description of current approaches is given. The underlying reasoning process is treated independently and divided into two parts. This includes a description of methods for hypotheses generation and methods for finding the best explanations among a set of possible ones. Furthermore, the complexity of the abductive task is surveyed in connection with its relationship to default reasoning. We conclude with the presentation of applications of the discussed approaches focusing on plan recognition and plan generation