While it has been realized for quite some time within AI that abduction is a general model of explanation for a variety of tasks, there have been no empirical investigations into the practical feasibility of a general, logic-based abductive approach to explanation. In this paper we present extensive empirical results on applying a general abductive system, Accel, to moderately complex problems in plan recognition and diagnosis. In plan recognition, Accel has been tested on 50 short narrative texts, inferring characters ' plans from actions described in a text. In medical diagnosis, Accel has diagnosed 50 real-world patient cases involving brain damage due to stroke (previously addressed by set-covering methods). Accel also uses abduction to accomplish model-based diagnosis of logic circuits (a full adder) and continuous dynamic systems (a temperature controller and the water balance system of the human kidney). The results indicate that general purpose abduction is an e ective and e cient mechanism for solving problems in plan recognition and diagnosis. 1

Several approaches have been proposed to deal with time in diagnosis. The goal of this paper is to propose a logical characterization of diagnosis with temporal knowledge, and, specifically, diagnosis with temporal constraints on the evolution of the system to be diagnosed. The characterization is independent of the specific temporal constraint language being used and is an extension of an abductive characterization of atemporal diagnosis. In a companion paper [ 4 ] we discuss a computational characterization of a restriction of the framework, based on the co-operation of an abductive and a temporal reasoner. 1 Introduction The need of taking into account the temporal dimension in model-based diagnosis has been advocated by many researchers (see, e.g., chapter 6 in [ 17 ] ). While a static model describes the correct and/or faulty behavior of a system (or of its components), at least two different (but related) dimensions of time have been considered in the approaches proposed so far:...

This paper presents a simple framework for Hornclause abduction, with probabilities associated with hypotheses. It is shown how this representation can represent any probabilistic knowledge representable in a Bayesian belief network. The main contributions are in finding a relationship between logical and probabilistic notions of evidential reasoning. This can be used as a basis for a new way to implement Bayesian Networks that allows for approximations to the value of the posterior probabilities, and also points to a way that Bayesian networks can be extended beyond a propositional language. 1

Abstract. The Independent Choice Logic began in the early 90’s as a way to combine logic programming and probability into a coherent framework. The idea of the Independent Choice Logic is straightforward: there is a set of independent choices with a probability distribution over each choice, and a logic program that gives the consequences of the choices. There is a measure over possible worlds that is defined by the probabilities of the independent choices, and what is true in each possible world is given by choices made in that world and the logic program. ICL is interesting because it is a simple, natural and expressive representation of rich probabilistic models. This paper gives an overview of the work done over the last decade and half, and points towards the considerable work ahead, particularly in the areas of lifted inference and the problems of existence and identity. 1

ion Axioms ? Luca Console 1 and Daniele Theseider Dupr'e 2 1 Dipartimento di Matematica e Informatica, Universit`a di Udine Via Zanon 6, 33100 Udine, Italy 2 Dipartimento di Informatica, Universit`a di Torino, Corso Svizzera 185, 10149 Torino, Italy Abstract. This paper deals with abductive reasoning on knowledge bases that are expressed at different levels of abstraction, but are not necessarily organized as a set of increasingly more abstract models, each one giving a complete (even if abstracted) description of a domain. We claim that the search for abductive explanations in such a context and, in particular, the choice of the "right" level at which explanations have to be determined, should be driven by the available observations in such a way that explanations involving low-level phenomena are allowed only if there are specific observations related to them, or higher-level explanations cannot be found. We present formal definitions following this principle and we discuss ho...

Knowledge-level (KL) modeling can be characterised as theory subset extraction where the extracted subset is consistent and relevant to some problem. Theory subset extraction is a synonym for Newell's principle of rationality, Clancey's model construction operators, and Breuker's components of expert solutions. In an abductive framework, a PSM is the extraction controller and is represented by a suite of BEST inference assessment operators. Each BEST operator is a single-classification expert system which accepts or culls a possible inference. PSMs can therefore be maintained by rippledown -rules, a technique for maintaining singleclassification expert systems. 1 Introduction Newell's knowledge-level (KL) approach modeled intelligence [37] as a search for appropriate operators that convert some current state to a goal state. Domain-specific knowledge are used to select the operators according to the principle of rationality; i.e. an intelligent agent will select an operator which i...

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AL-TR-1995-XXXX Approved for Public Release; distribution is unlimited A This document provides a method overview, practice and use description, and language reference for the Integration Definition (IDEF) method for Process Description Capture (IDEF3). IDEF3 is designed to help document and analyze the processes of an existing or proposed system. Proven guidelines and a language for information capture help users capture and organize process information for multiple downstream uses. IDEF3 supports both process-centered and object-centered knowledge acquisition strategies enabling users to capture assertions about real-world processes and events in ways paralleling common forms of human expression. IDEF3 includes the ability to capture and structure descriptions of how a system works from multiple viewpoints. As an integral member of the IDEF family of methods, IDEF3 works well in independent application or in concert with other IDEF methods to document, analyze,

In this chapter I review Bayesian statistics as used for induction and relate it to logic-based abduction. Much reasoning under uncertainty, including induction, is based on Bayes' rule. Bayes' rule is interesting precisely because it provides a mechanism for abduction. I review work of Buntine that argues that much of the work on Bayesian learning can be best viewed in terms of graphical models such as Bayesian networks, and review previous work of Poole that relates Bayesian networks to logic-based abduction. This lets us see how much of the work on induction can be viewed in terms of logic-based abduction. I then explore what this means for extending logic-based abduction to richer representations, such as learning decision trees with probabilities at the leaves. Much of this paper is tutorial in nature; both the probabilistic and logic-based notions of abduction and induction are introduced and motivated. 1 Introduction This paper explores the relationship between learning (induct...

In the framework of meeting scheduling problems, we present an approach where for every agent the behavior of other agents is explained in terms of a common cognitive structure. This structure accounts for the combination of emotional and intellectual factors which produce a particular behavior when confronted to a particular situation generated by an ecology of truly decentralized agents, interacting in a concurrent way. The cognitive structure is translated into a computational architecture intended for empirical experimentation. We present a research perspective aimed to investigate group adaptation and evolution as a consequence of the refinement of every agent cognitive model that each agent maintain. This research has been sponsored in part by CONACYT and ITESM Campus Monterrey. x This paper appears in the proceedings of the First Iberoamerican Workshop on Distributed Artificial Intelligence and Multiagent Systems, LANIA & MIA-UV, 1996. 1 Introduction In Psychology, two br...