This paper presents a simple framework for Horn-clause abduction, with probabilities associated with hypotheses. The framework incorporates assumptions about the rule base and independence assumptions amongst hypotheses. It is shown how any probabilistic knowledge representable in a discrete Bayesian belief network can be represented in this framework. The main contribution is in finding a relationship between logical and probabilistic notions of evidential reasoning. This provides a useful representation language in its own right, providing a compromise between heuristic and epistemic adequacy. It also shows how Bayesian networks can be extended beyond a propositional language. This paper also shows how a language with only (unconditionally) independent hypotheses can represent any probabilistic knowledge, and argues that it is better to invent new hypotheses to explain dependence rather than having to worry about dependence in the language. Scholar, Canadian Institute for Advanced...

Although there are many arguments that logic is an appropriate tool for artificial intelligence, there has been a perceived problem with the monotonicity of classical logic. This paper elaborates on the idea that reasoning should be viewed as theory formation where logic tells us the consequences of our assumptions. The two activities of predicting what is expected to be true and explaining observations are considered in a simple theory formation framework. Properties of each activity are discussed, along with a number of proposals as to what should be predicted or accepted as reasonable explanations. An architecture is proposed to combine explanation and prediction into one coherent framework. Algorithms used to implement the system as well as examples from a running implementation are given. Key words: defaults, conjectures, explanation, prediction, abduction, dialectics, logic, nonmonotonicity, theory formation Explanation and Prediction 2 1 Introduction One way to do research i...

Is there one logical definition of diagnosis? In this paper I argue that the answer to this question is "no". This paper is about the pragmatics of using logic for diagnosis; we show how two popular proposals for using logic for diagnosis, (namely abductive and consistency-based approaches) can be used to solve diagnostic tasks. The cases with only knowledge about how normal components work (any deviation being an error) and where there are fault models (we try to find a covering of the observations) are considered as well as the continuum between. The result is that there are two fundamentally different, but equally powerful diagnostic paradigms. They require different knowledge about the world, and different ways to think about a domain. This result indicates that there may not be an axiomatisation of a domain that is independent of how the knowledge is to be used.

Despite their different perspectives, artificial intelligence (AI) and the disciplines of decision science have common roots and strive for similar goals. This paper surveys the potential for addressing problems in representation, inference, knowledge engineering, and explanation within the decision-theoretic framework. Recent analyses of the restrictions of several traditional AI reasoning techniques, coupled with the development of more tractable and expressive decisiontheoretic representation and inference strategies, have stimulated renewed interest in decision theory and decision analysis. We describe early experience with simple probabilistic schemes for automated reasoning, review the dominant expert-system paradigm, and survey some recent research at the crossroads of AI and decision science. In particular, we present the belief network and influence diagram representations. Finally, we discuss issues that have not been studied in detail within the expert-systems sett...

This paper investigates two different activities that involve making assumptions: predicting what one expects to be true and explaining observations. In a companion paper, an architecture for both prediction and explanation is proposed and an implementation is outlined. In this paper, we show how such a hypothetical reasoning system can be used to solve recognition, diagnostic and prediction problems. As part of this is the assumption that the default reasoner must be "programmed" to get the right answer and it is not just a matter of "stating what is true" and hoping the system will magically find the right answer. A number of distinctions have been found in practice to be important: between predicting whether something is expected to be true versus explaining why it is true; and between conventional defaults (assumptions as a communication convention), normality defaults (assumed for expediency) and conjectures (assumed only if there is evidence). The effects of these distinctions on...

If one wants to use logic to build a diagnostic system, then it is not a matter of "just axiomatising" the domain; we have to understand how to use logic for diagnosis. We need some models of what diagnosis is, in order to be able to implement diagnostic systems. This paper considers 3 different "logical " definitions of diagnosis. Each of these are presented in a uniform framework of hypothetical reasoning where the user provides the possible hypotheses. These are compared as to the sort of knowledge that we need to provide them, and in their expressibilty. It seems as though there is no one framework which can claim to be the logical definition of diagnosis. Each of these approaches has been implemented in the Theorist system, and used on a number of domains. This paper concentrates on the case where we have fault models. 1 Introduction Diagnosis is a problem of trying to find what is wrong with some system based on knowledge about the design /structure of the system, possible malf...

Artificial intelligence researchers have been designing representation systems for default and abductive reasoning. Logic Programming researchers have been working on techniques to improve the efficiency of Horn Clause deduction systems. This paper describes how one such default and abductive reasoning system (namely Theorist) can be translated into Horn clauses (with negation as failure), so that we can use the clarity of abductive reasoning systems and the efficiency of Horn clause deduction systems. We thus show how advances in expressive power that artificial intelligence workers are working on can directly utilise advances in efficiency that logic programming researchers are working on. Actual code from a running system is given. 1 Introduction Many people in Artificial Intelligence have been working on default reasoning and abductive diagnosis systems [35, 20, 4, 29]. The systems implemented so far (eg., [1, 16, 12, 34, 32]) are only prototypes or have been developed in A Theo...

This paper presents a simple logical framework for abduction, with probabilities associated with hypotheses. The language is an extension to pure Prolog, and it has straight-forward implementations using branch and bound search with either logic-programming technology or ATMS technology. The main focus of this paper is arguing for a form of representational adequacy of this very simple system for diagnostic reasoning. It is shown how it can represent model-based knowledge, with and without faults, and with and without non-intermittency assumptions. It is also shown how this representation can represent any probabilistic knowledge representable in a Bayesian belief network. 1

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

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