Abductive reasoning

About: Abductive reasoning is a research topic. Over the lifetime, 1917 publications have been published within this topic receiving 44645 citations. The topic is also known as: abduction & abductive inference.

Foundations of Knowledge Representation and Reasoning

Abstract: Foundations of knowledge representation and reasoning.- Collective entities and relations in concept languages.- Computing extensions of terminological default theories.- A formalization of interval-based temporal subsumption in first order logic.- Normative, subjunctive and autoepistemic defaults.- Abductive reasoning with abstraction axioms.- Queries, rules and definitions as epistemic sentences in concept languages.- The power of beliefs or translating default logic into standard autoepistemic logic.- Learning an optimally accurate representation system.- Default reasoning via negation as failure.- Weak autoepistemic reasoning and well-founded semantics.- Forming concepts for fast inference.- A common-sense theory of time.- Reasoning with analogical representations.- Asking about possibilities - Revision and update semantics for subjunctive queries Extended report.- On the impact of stratification on the complexity of nonmonotonic reasoning.- Logics of mental attitudes in AI.- Hyperrational conditionals.- Revision by expansion in logic programs.

Monological reason-based logic: a low level integration of rule-based reasoning and case-based reasoning

Abstract: This paper contains an informal introduction to a theory about legal reasoning (reason-based logic) that takes the notion of a reason to be central. Arguing for a conclusion comes down to first collecting the reasons that plead for and against the conclusion, and second weighing them. The paper describes how we can establish the presence of a reason and how we can argue whether the reasons for or the reasons against the conclusion prevail. It also addresses the topic of meta-level reasoning about the use of rules in concrete cases. It is shown how both rule-based reasoning and case-based reasoning are naturally incorporated in the theory of reason-based logic.

GALGO: A Genetic ALGOrithm Decision Support Tool for Complex Uncertain Systems Modeled with Bayesian Belief Networks

Abstract: Bayesian belief networks can be used to represent and to reason about complex systems with uncertain, incomplete and conflicting information. Belief networks are graphs encoding and quantifying probabilistic dependence and conditional independence among variables. One type of reasoning of interest in diagnosis is called abductive inference (determination of the global most probable system description given the values of any partial subset of variables). In some cases, abductive inference can be performed with exact algorithms using distributed network computations but it is an NP-hard problem and complexity increases drastically with the presence of undirected cycles, number of discrete states per variable, and number of variables in the network. This paper describes an approximate method based on genetic algorithms to perform abductive inference in large, multiply connected networks for which complexity is a concern when using most exact methods and for which systematic search methods are not feasible. The theoretical adequacy of the method is discussed and preliminary experimental results are presented.

Using compiled knowledge to guide and focus abductive diagnosis

Abstract: Several artificial intelligence architectures and systems based on "deep" models of a domain have been proposed, in particular for the diagnostic task. These systems have several advantages over traditional knowledge based systems, but they have a main limitation in their computational complexity. One of the ways to face this problem is to rely on a knowledge compilation phase, which produces knowledge that can be used more effectively with respect to the original one. We show how a specific knowledge compilation approach can focus reasoning in abductive diagnosis, and, in particular, can improve the performances of AID, an abductive diagnosis system. The approach aims at focusing the overall diagnostic cycle in two interdependent ways: avoiding the generation of candidate solutions to be discarded a posteriori and integrating the generation of candidate solutions with discrimination among different candidates. Knowledge compilation is used off-line to produce operational (i.e., easily evaluated) conditions that embed the abductive reasoning strategy and are used in addition to the original model, with the goal of ruling out parts of the search space or focusing on parts of it. The conditions are useful to solve most cases using less time for computing the same solutions, yet preserving all the power of the model-based system for dealing with multiple faults and explaining the solutions. Experimental results showing the advantages of the approach are presented.

A Probabilistic Approach to Language Understanding

Abstract: We discuss a new framework for text understanding. Three major design decisions characterize this approach. First, we take the problem of text understanding to be a particular case of the general problem of abductive inference: reasoning from effects to causes. Second, we use probability theory to handle the uncertainty which arises in abductive inference in general, and natural language understanding in particular. Finally, we treat all aspects of the text understanding problem in a unified way. All aspects of natural language processing are treated in the same framework, allowing us to integrate syntactic, semantic and pragmatic constraints. In order to apply probability theory to this problem, we have developed a probabilistic model of text understanding. To make it practical to use this model, we have devised a way of incrementally constructing and evaluating belief networks that is applicable to other abduction problems. We have written a program, Wimp3, to experiment with this framework.