A theory of analogical mapping between source and target analogs based upon Interacting structural, semantic, and pragmatic constraints is proposed here. The structural constraint of isomorphism encourages mappings that maximize the consistency of relational corresondences between the elements of the two analogs. The constraint of semantic similarity supports mapping hypotheses to the degree that mapped predicates have similar meanings. The constraint of prog-mafic central/! / favors mappings involving elements the analogist believes to be Important in order to achieve the purpose for which the analogy Is being used. The theory is implemented in a computer program called ACME (Analogical Constraint Mapping Engine), which represents constraints by means of a network of supporting and competing hypotheses regarding what elements to map. A coop-erative algorithm for parallel constraint satisfaction identifies mapping hypotheses that collectively represent the overall mapping that best fits the interacting constraints. ACME has been applied to a wide range of examples that include problem analogies, analogical arguments, explanatory analogies, story analogies, formal analogies, and metaphors. ACME is sensitive to semantic and pragmatic Information if it Is available,.and yet able to compute mappings between formally Isomorphic analogs without any similar or identical elements. The theory Is able to account for empirical findings regarding the impact of consistency and similarity on human processing of analogies.

The problem of abduction can be characterized as finding the best explanation of a set of data. In this paper we focus on one type of abduction in which the best explanation is the most plausible combination of hypotheses that explains all the data. We then present several computational complexity results demonstrating that this type of abduction is intractable (NP-hard) in general. In particular, choosing between incompatible hypotheses, reasoning about cancellation effects among hypotheses, and satisfying the maximum plausibility requirement are major factors leading to intractability. We also identify a tractable, but restricted, class of abduction problems. Thanks to B. Chandrasekaran, Ashok Goel, Jack Smith, and Jon Sticklen for their comments on the numerous versions of this paper. The referees have also made a substantial contribution. Any remaining errors are our responsibility, of course. This research has been supported in part by the National Library of Medicine, grant LM-...

Officer praised dog for barking at object." Enables Detect Drugs out FK Initiates Retrieval 5 6 Missing Figure 10. Forgetting to fill the tank with gas A=actual intention; E=expectation; Q=question; C=context; I=index; G=goal Tank Out of Gas Tank Full Tank Low Fill Tank Should have filled up with gas when tank low Expectation What Action to Do? KEY: G = goal; I = index; C = context; Q = question; E = expectation; A = actual intention Results At Store connections with related concepts. Other learning goals take multiple arguments. For instance, a knowledge differentiation goal (Cox & Ram, 1995) is a goal to determine a change in a body of knowledge such that two items are separated conceptually. In contrast, a knowledge reconciliation goal (Cox & Ram, 1995) is one that seeks to merge two items that were mistakenly considered separate entities. Both expansion goals and reconciliation goals may include or spawn a knowledge organization goal (Ram, 1993) that seeks to reorganize the existing knowledge so that it is made available to the reasoner at the appropriate time, as well as modify the structure or content of a concept itself. Such reorganization of knowledge affects the conditions under which a particular piece of knowledge is retrieved or the kinds of indexes associated with an item in memory.

Abduction is a methodology of scientific researches. Peirce showed three types of abduction, and expressed them by one syllogism. Recently various researches on abduction or abductive logic have been developed in the fields of automated reasoning and machine learning. In order to systematically understand such researches and to clearly discuss abduction, this paper classifies abduction into five types. This new classification is based on an interpretation of the syllogism in abduction and the definitions of hypotheses. We examine various researches on abduction so far developed and show that many researches on abduction can be placed in our classification. Furthermore, we discuss the most essential type of abduction in our classification for logic programming and default logic, and describe Prolog programs for the abduction. 1 Introduction Charles Sanders Peirce, who was a philosopher, scientist and logician, asserted that a scientific research consists of three stages, abduction, ded...

In this paper, we propose a reconstruction of logic-based approaches to abductive reasoning in terms of ampliative adaptive logics. The advantages of this reconstruction are important: the resulting logics have a proper proof theory (that leads to justified conclusions even for undecidable fragments), they nicely integrate deductive and abductive steps, and they are much closer to natural reasoning than the existing systems.

Abstract not found

Causal reasoning can be understood qualitatively in terms of explanatory coherence or quantitatively in terms of probability theory. Comparison of these approaches can be done by looking at computational models, using my explanatory coherence networks and Pearl’s probabilistic ones. The explanatory coherence program ECHO can be given a probabilistic interpretation, but there are many conceptual and computational problems that make it difficult to replace coherence networks by probabilistic ones. On the other hand, ECHO provides a psychologically plausible and computationally efficient model of some kinds of probabilistic causal reasoning. Hence coherence theory need not give way to probability theory as the basis for epistemology and decision making.

As knowledge-based systems are addressing increasingly complex domains, their roles are shifting from classical expert systems to interactive assistants. To develop and maintain such systems, an integration of thorough knowledge acquisition procedures and sustained learning from experience is called for. A knowledge level modeling perspective has shown to be useful for analyzing the various types of knowledge related to a particular domain and set of tasks, and for constructing the models of knowledge contents needed in an intelligent system. To be able to meet the requirements of future systems with respect to robust competence and adaptive learning behavior, particularly in open and weak theory domains, a stronger emphasis should be put on the combined utilization of casespecific and general domain knowledge. In this chapter we present a framework for integrating KA and ML methods within a total knowledge modeling cycle, favoring an iterative rather than a top down approac...

A biochemical pathway is a sequence of chemical reactions in a biological organism. Such pathways specify mechanisms that explain how cells carry out their major functions by means of molecules and reactions that produce regular changes. Many diseases can be explained by defects in pathways, and new treatments often involve finding drugs that correct those defects. This paper presents explanation schemas and treatment strategies that characterize how thinking about pathways contributes to biomedical discovery. It discusses the significance of pathways for understanding the nature of diseases, explanations, and theories.

175 Bibliography 177 viii Acknowledgements It is a privilege to have five professors on my reading committee representing the different areas of my Ph.D. program in Philosophy and Symbolic Systems: Computer Science, Linguistics, Logic, Philosophy, and Psychology. Tom Wasow was the first person to point me in the direction of abduction. He gave me useful comments on earlier versions of this dissertation, always insisting that it be readable to non experts. It was also a pleasure to work with him this last year coordinating the undergraduate Symbolic Systems program. Dagfinn Føllesdal encouraged me to continue exploring connections between abduction and philosophy of science. Yoav Shoham and Pat Suppes gave me very good advice about future expansions of this work. Jim Greeno chaired my defense and also gave me helpful suggestions. For their help with my dissertation, and for the classes in which they taught me, I am very grateful. To my advisor, Johan van Benthem, I offer my deepest gr...