Showing papers on "Abductive reasoning published in 1995"

Commitment in Dialogue: Basic Concepts of Interpersonal Reasoning

Abstract: Thank you very much for reading commitment in dialogue basic concepts of interpersonal reasoning. As you may know, people have look hundreds times for their favorite readings like this commitment in dialogue basic concepts of interpersonal reasoning, but end up in harmful downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some harmful virus inside their laptop.

The repair of speech act misunderstandings by abductive inference

Abstract: During a conversation, agents can easily come to have different beliefs about the meaning or discourse role of some utterance. Participants normally rely on their expectations to determine whether the conversation is proceeding smoothly: if nothing unusual is detected, then understanding is presumed to occur. Conversely, when an agent says something that is inconsistent with another's expectations, then the other agent may change her interpretation of an earlier turn and direct her response to the reinterpretation, accomplishing what is known as a fourth-turn repair.Here we describe an abductive account of the interpretation of speech acts and the repair of speech act misunderstandings. Our discussion considers the kinds of information that participants use to interpret an utterance, even if it is inconsistent with their beliefs. It also considers the information used to design repairs. We describe a mapping between the utterance-level forms (semantics) and discourse-level acts (pragmatics), and a relation between the discourse acts and the beliefs and intentions that they express. We specify for each discourse act, the acts that might be expected, if the hearer has understood the speaker correctly. We also describe our account of belief and intention, distinguishing the beliefs agents actually have from the ones they act as if they have when they perform a discourse act. To support repair, we model how misunderstandings can lead to unexpected actions and utterances and describe the processes of interpretation and repair. To illustrate the approach, we show how it accounts for an example repair.

Abduction, experience, and goals: a model of everyday abductive explanation

Abstract: Many abductive understanding systems generate explanations by a backwards chaining process that is neutral both to the explainer's previous experience in similar situations and to why the explainer is attempting to explain. This article examines the relationship of such models to an approach that uses case-based reasoning to generate explanations. In this case-based model, the generation of abductive explanations is focused by prior experience and by goal-based criteria reflecting current information needs. The article analyses the commitments and contributions of this case-based model as applied to the task of building good explanations of anomalous events in everyday understanding. The article identifies six central issues for abductive explanation, compares how these issues are addressed in traditional and case-based explanation models, and discusses benefits of the case-based approach for facilitating generation of plausible and useful explanations in domains that are complex and imperfectly ...

ADAPtER: An Integrated Diagnostic System Combining Case-Based and Abductive Reasoning

Abstract: The aim of this paper is to describe the ADAPtER system, a diagnostic architecture combining case-based reasoning with abductive reasoning and exploiting the adaptation of the solution of old episodes, in order to focus the reasoning process. Domain knowledge is represented via a logical model and basic mechanisms, based on abductive reasoning with consistency constraints, have been defined for solving complex diagnostic problems involving multiple faults. The model-based component has been supplemented with a case memory and adaptation mechanisms have been developed, in order to make the diagnostic system able to exploit past experience in solving new cases. A heuristic function is proposed, able to rank the solutions associated to retrieved cases with respect to the adaptation effort needed to transform such solutions into possible solutions for the current case. We will discuss some preliminary experiments showing the validity of the above heuristic and the convenience of solving a new case by adapting a retrieved solution rather than solving the new problem from scratch.

Semantics and complexity of abduction from default theories

Abstract: Since logical knowledge representation is commonly based on nonclassical formalisms like default logic, autoepistemic logic, or circumscription, it is necessary to perform abductive reasoning from theories of nonclassical logics. In this paper, we investigate how abduction can be performed from theories in default logic. Different modes of abduction are plausible, based on credulous and skeptical default reasoning; they appear useful for different applications such as diagnosis and planning. Moreover, we analyze the complexity of the main abductive reasoning tasks. They are intractable in the general case; we also present known classes of default theories for which abduction is tractable.

The Logic of Partial Information

Abstract: This book presents the foundations of reasoning with partial information and a theory of common sense reasoning based on monotonic logic and partial structures. This theory was designed specifically for the needs of practicing computer scientists and provides easily implementable algorithms. Starting from first principles, following the logic of discovery of Karl Popper and Imre Lakatos, and the semantics of programming languages, the book develops a system of reasoning with partial information, and applies it to a comprehensive study of the problem examples from the literature of common sense reasoning. Proof-theoretic and model-theoretic views are considered in the applications, as well as logical problems of theoretical physics, such as issues related to Heisenberg's uncertainty principle. The book points out that customary expositions of common-sense reasoning are based on a flawed non-monotonic reasoning paradigm and that the resulting solutions proposed for major problems, such as the frame problem, are either ad hoc or inadequate. It is shown that non-monotonicity results from hiding information that should not be hidden. The essential research in common-sense reasoning has been developed in isolation from the disciplines of theoretical computer science and classical logic. This work breaks the isolation and establishes deep links. The book will be of interest to computer scientists, mathematicians, logicians, and philosophers interested in the foundations and applications of reasoning with partial information.

Unified logic programming approach to the abduction of plans and intentions in information-seeking dialogues

Abstract: We propose a framework that supports the recognition of plans and intentions behind speech acts through abductive inferences over discourse sentences. These inferences allow each agent to have an active and intelligent participation in dialogues, namely, in cooperative information-seeking dialogues. In our framework, the possible actions, events, states, and world knowledge are represented by extended logic programs (LP with explicit negation), and the abductive inference porcess is modeled by the framework proposed by Pereira et al. [13], which is based on the Well Founded Semantics augmented with explicit negation (WFSX) and contradiction removal semantics (CRSX). It will be shown how this framework supports abductive planning with Event Calculus [5], and some examples will be shown [10, 14] in the domain of information-seeking dialogues. Finally, some open problems will be pointed out.

AILP abductive inductive logic programming

Abstract: Inductive Logic Programming (ILP) is often situated as a research area emerging at the intersection of Machine Learning and Logic Programming (LP). This paper makes the link more clear between ILP and LP, in particular, between ILP and Abductive Logic Programming (ALP), i e, LP extended with abductive reasoning. We formulate a generic framework for handling incomplete knowledge. This framework can be instantiated both to ALP and ILP approaches. By doing so more light is shed on the relationship between abduction and induction. As an example we consider the abductive procedure SLDNFA, and modify it into an inductive procedure which we call SLDNFAI.

A Terminological Interpretation of (Abductive) Logic Programming

Abstract: The logic program formalism is commonly viewed as a modal or default logic. In this paper, we propose an alternative interpretation of the formalism as a terminological logic. A terminological logic is designed to represent two different forms of knowledge. A TBox represents definitions for a set of concepts. An ABox represents the assertional knowledge of the expert. In our interpretation, a logic program is a TBox providing definitions for all predicates; this interpretation is present already in Clark's completion semantics. We extend the logic program formalism such that some predicates can be left undefined and use classical logic as the language for the ABox. The resulting logic can be seen as an alternative interpretation of abductive logic program formalism. We study the expressivity of the formalism for representing uncertainty by proposing solutions for problems in temporal reasoning, with null values and open domain knowledge.

Belief Revision, Conditional Logic and Nonmonotonic Reasoning

Abstract: We consider the connections between belief revision, conditional logic and nonmonotonic reasoning, using as a foundation the approach to theory change developed by Alchourron, Gardenfors and Makinson (the AGM approach). This is first generalized to allow the iteration of theory change operations to capture the dynamics of epistemic states according to a principle of minimal change of entrenchment. The iterative operations of expansion, contraction and revision are characterized both by a set of postulates and by Grove’s construction based on total pre-orders on the set of complete theories of the belief logic. We present a sound and complete conditional logic whose semantics is based on our iterative revision operation, but which avoids Gardenfors’s triviality result because of a severely restricted language of beliefs and hence the weakened scope of our extended postulates. In the second part of the paper, we develop a computational approach to theory dynamics using Rott’s E-bases as a representation for epistemic states. Under this approach, a ranked E-base is interpreted as standing for the most conservative entrenchment compatible with the base, reflecting a kind of foundationalism in the acceptance of evidence for a belief. Algorithms for the computation of our iterative versions of expansion, contraction and revision are presented. Finally, we consider the relationship between nonmonotonic reasoning and both conditional logic and belief revision. Adapting the approach of Delgrande, we show that the unique extension of a default theory expressed in our conditional logic of belief revision corresponds to the most conservative belief state which respects the theory: however, this correspondence is limited to propositional default theories. Considering first order default theories, we present a belief revision algorithm which incorporates the assumption of independence of default instances and propose the use of a base logic for default reasoning which incorporates uniqueness of names. We conclude with an examination of the behavior of an implemented system on some of Lifschitz’s benchmark problems in nonmonotonic reasoning.

Using abductive inference and dynamic indexing to retrieve multimedia SGML documents

Abstract: The retrieval of complex multimedia items such as SGML-structured texts can be facilitated by means of a formal representation of knowledge about these data. These information sources must be aggregated dynamically at the time of query processing. In this paper, an interactive, probabilistic retrieval system is proposed, comprising an extended Bayesian network, a multimedia indexing component and an abductive retrieval engine. The inference process exploits and controls the index structure of the network. The prototype has been tested on a collection of SGML structured dictionary articles. An example is presented in the last section of the paper.

A study on fuzzy abductive inference

Abstract: This paper proposes a fuzzy abductive inference method to realize a creative thinking support system. The fuzzy logic is applied to Peng and Reggia's (1990) abductive inference for handling degrees of manifestations. Application of the new method to a diagnostic problem is shown and the effectiveness of the proposed method is demonstrated by comparing the inferred results with those by the conventional fuzzy abduction. >

Practical abduction: characterization, decomposition and concurrency

Abstract: Abductive inferences seem to be ubiquitous in cognition, and cognitive agents often solve complex abduction tasks very rapidly. However, abduction characterized as ‘inference to the best explanation’ is in general computationally intractable. This paper describes three related ideas for understanding how intelligent agents might efficiently perform abduction tasks. First, we recharacterize the abduction task as inference to a confident explanation, where a confident explanation is internally consistent, parsimonious, distinctly more plausible than alternative explanations, and explains as much of the data as possible with high confidence. Second, we describe a decomposition of the task of synthesizing a confident explanation into several subtasks so that the synthesis starts from islands of relative certainty and then grows opportunistically. This decomposition helps in controlling the computational cost of accommodating interactions among explanatory hypotheses, especially incompatibility intera...

The Role of Abductive Logic in Understanding and Using Advanced Empathy

Abstract: Abductive reasoning, which is the basis of hypothesis formation, holds promise for facilitating the development of advanced empathy in counselor trainees.

Evaluating Case-Based Reasoning Systems

Abstract: Evaluation is an important issue for every scientific field and a necessity for an emerging software technology like case-based reasoning. This paper is a supplementation to the review of industrial case-based reasoning tools by K.-D. Althoff, E. Auriol, R. Barletta and M. Manago which describes the most detailed evaluation of commercial case-based reasoning tools currently available. The author focuses on some important aspects that correspond to the evaluation of case-based reasoning systems and gives links to ongoing research.

The Specter of Metaphysics in Counseling Research and Practice: The Qualitative Challenge

Abstract: After decades of scientific research, the specter of metaphysics pervades and hinders virtually all aspects of counseling research, theory, and practice. Qualitative research is suggested as capable of coming to terms with such issues. This article notes that qualitative research will gain acceptance when it takes a significant step toward solving a major problem in the discipline. A tentative outline of an approach to metaphysical problems involving radical empiricism and phenomenology is provided. Along the way, some necessary cognitive shifts are delineated, involving dialectical and abductive reasoning processes.

Dialogue strategies for multimedia retrieval: intertwining abductive reasoning and dialogue planning

Abstract: Multimedia information retrieval is an inherently interactive process. When the user enters a conceptual query, that is, a specification of some information need in terms of abstract concepts, there may be many alternative ways of interpreting the query. This, in turn, affects the determination ofwhat items are relevant for retrieval. In the MIRACLE system, these problems are tackled by combining abductive reasoning and dialogue planning. Whereas MIRACLE's abductive retrieval engine is capable of deriving different interpretations of ambiguous queries, the dialogue planner employs a comprehensive conversational dialogue model for negotiating queries, clarifying information needs, and explaining retrieval results.

Reasoning paradigms in legal decision support systems

Abstract: In this paper we discuss the strengths and weaknesses of a range of artificial intelligence approaches used in legal domains. Symbolic reasoning systems which rely on deductive, inductive and analogical reasoning are described and reviewed. The role of statistical reasoning in law is examined, and the use of neural networks analysed. There is discussion of architectures for, and examples of, systems which combine a number of these reasoning strategies. We conclude that to build intelligent legal decision support systems requires a range of reasoning strategies.

The Effect of Partial Deduction in Abductive Reasoning.

Abstract: Partial deduction is known as an optimization technique in logic programming. In the context of abductive logic programming, however, we present in this paper that normal partial deduction does not preserve explanations for abductive reasoning. Then we provide an alternative method of partial deduction, called abductive partial deduction, which is shown to preserve the meanings of abductive logic programs. A method of partial abduction is also introduced as an optimization for abductive reasoning in logic programs.

Deductive, inductive and analogical reasoning in legal decision support systems

Abstract: In this paper we provide an overview of a number of fundamental reasoning formalisms in artificial intelligence which can and have been used in modelling legal reasoning. We describe deduction, induction and analogical reasoning formalisms, and show how they can be used separately to model legal reasoning. We argue that these formalisms can be used together to model legal reasoning more accurately, and describe a number of attempts to integrate the approaches.

Complexity Results for Abductive Logic Programming

Abstract: In this paper, we argue that logic programming semantics can be more meaningful for abductive reasoning than classical inference by providing examples from the area of knowledge representation and reasoning. The main part of the paper addresses the issue of the computational complexity of the principal decisional problems in abductive reasoning, which are: Given an instance of an abduction problem (i) does the problem have solution (i.e., an explanation); (ii) does a given hypothesis belong to some explanation; and (iii) does a given hypothesis belong to all explanations. These problems are investigated here for the stable model semantics of normal logic programs.

An abductive network for predicting tool life in drilling

Abstract: The paper presents an abductive network for predicting tool life in drilling operations. The abductive network is composed of a number of functional nodes. These functional nodes are well organized to form an optimal network architecture by using a predicted squared error (PSE) criterion. Once the drill diameter, cutting speed, and feedrate are given, the tool life can be predicted based on the developed network. Experimental results have shown that the abductive network can be effectively used to predict drill life under varying cutting conditions and the prediction error of drill life is less than 9%.

Reasoning paradigms in legal decision support systems

Abstract: In this paper we discuss the strengths and weaknesses of a range of artificial intelligence approaches used in legal domains. Symbolic reasoning systems which rely on deductive, inductive and analogical reasoning are described and reviewed. The role of statistical reasoning in law is examined, and the use of neural networks analysed. There is discussion of architectures for, and examples of, systems which combine a number of these reasoning strategies. We conclude that to build intelligent legal decision support systems requires a range of reasoning strategies.