Showing papers on "Abductive reasoning published in 1994"

Interpretation as abduction

Abstract: Abduction is inference to the best explanation. In the TACITUS project at SRI we have developed an approach to abductive inference, called “weighted abduction”, that has resulted in a significant simplification of how the problem of interpreting texts is conceptualized. The interpretation of a text is the minimal explanation of why the text would be true. More precisely, to interpret a text, one must prove the logical form of the text from what is already mutually known, allowing for coercions, merging redundancies where possible, and making assumptions where necessary. It is shown how such “local pragmatics” problems as reference resolution, the interpretation of compound nominals, the resolution of syntactic ambiguity and metonymy, and schema recognition can be solved in this manner. Moreover, this approach of “interpretation as abduction” can be combined with the older view of “parsing as deduction” to produce an elegant and thorough integration of syntax, semantics, and pragmatics, one that spans the range of linguistic phenomena from phonology to discourse structure. Finally, we discuss means for making the abduction process efficient, possibilities for extending the approach to other pragmatics phenomena, and the semantics of the weights and costs in the abduction scheme.

Abductive Inference : Computation, Philosophy, Technology

Abstract: Introduction 1. Conceptual analysis of abduction: what is abduction? 2. Knowledge-based systems and the science of AI: 3. Two RED systems 4. Generalizing the control strategy 5. More kinds of knowledge: TIPS and PATHEX/LIVER TIPS 6. Better task analysis, better strategy 7. Computational complexity of abduction 8. Diagnostic systems MDX2 and QUADS 9. Practical abduction 10. Perception and language understanding Appendices.

Logic programming and knowledge representation

Abstract: In this paper, we review recent work aimed at the application of declarative logic programming to knowledge representation in artificial intelligence. We consider extensions of the language of definite logic programs by classical (strong) negation, disjunction, and some modal operators and show how each of the added features extends the representational power of the language. We also discuss extensions of logic programming allowing abductive reasoning, meta-reasoning and reasoning in open domains. We investigate the methodology of using these languages for representing various forms of nonmonotonic reasoning and for describing knowledge in specific domains. We also address recent work on properties of programs needed for successful applications of this methodology such as consistency, categoricity and complexity.

Reasoning with models

Abstract: We develop a model-based approach to reasoning, in which the knowledge base is represented as a set of tnodels (satisfying assignments) rather then a logical formula, and the set of queries is restricted. We show that for every propositional knowledge base (KB) there exists a set of characteristic models with the property that a query is true in KB if and only if it is satisfied by the models in this set. We fully characterize a set of theories for which the model-based representation is compact and provides efficient reasoning. These include some cases where the formula-based representation does not support efficient reasoning. In addition, we consider the model-based approach to abductive reasoning and show that for any propositional KB, reasoning with its model-based representation yields an abductive explanation in time that is polynomial in its size.

Case-based reasoning

Abstract: Case-based reasoning (CBR) systems reason from experience: they solve new problems by retrieving relevant prior cases and adapting them to fit new situations. In 1988 the first case-based reasoning workshop, sponsored by DARPA, identified theoretical foundations and fundamental issues for case-based reasoning research. Since then, much investigation has examined the CBR process itself, the validity of CBR as a cognitive model, and the application of CBR technology. The results of that work include refinements in theories of the case-based reasoning process, psychological evidence for human case-based reasoning, and the fielding of over 100 CBR applications.

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.

Upside-down meta-interpretation of the model elimination theorem-proving procedure for deduction and abduction

Abstract: Typical bottom-up, forward-chaining reasoning systems such as hyperresolution lack goaldirectedness, while typical top-down, backward-chaining reasoning systems like Prolog or model elimination repeatedly solve the same goals. Reasoning systems that are goal-directed and avoid repeatedly solving the same goals can be constructed by formulating the top-down methods meta-theoretically for execution by a bottom-up reasoning system (hence, we use the term upside-down meta-interpretation). This formulation also facilitates the use of flexible search strategies, such as merit-ordered search, that are common to bottom-up reasoning systems. The model elimination theorem-proving procedure, its extension by an assumption rule for abduction, and its restriction to Horn clauses are adapted here for such upside-down meta-interpretation. This work can be regarded as an extension of the magic-sets or Alexander method for query evaluation in deductive databases to both non-Horn clauses and abductive reasoning.

REVISE: An Extended Logic Programming System for Revising Knowledge Bases

Abstract: In this paper we describe REVISE, an extended logic programming system for revising knowledge bases. REVISE is based on logic programming with explicit negation, plus a two-valued assumption revision to face contradiction, encompasses the notion of preference levels. Its reliance on logic programming allows efficient computation and declarativity, whilst its use of explicit negation, revision and preference levels enables modeling of a variety of problems including default reasoning, belief revision and model-based reasoning. It has been implemented as a Prolog-meta interpreter and tested on a spate of examples, namely the representation of diagnosis strategies in modelbased reasoning systems.

Hybrid case-based reasoning

Abstract: This paper reviews a number of hybrid Case-Based Reasoning (CBR) systems. These systems are hybrid because the CBR components cooperate with one or more “co-reasoners” which employ a different type of reasoning strategy (e.g. qualitative simulation, constraint satisfaction, etc.). In this paper, we propose that CBR is in fact an inherently hybrid process. We review a number of systems and identify three classes of architecture which have been used for hybrid systems. We believe that to successfully exploit a co-reasoner within a CBR system it is necessary to analyse where, when, why and how the information provided by the co-reasoner will be used. We propose the KADS methodology as a suitable way of performing such an analysis and illustrate its use by example. We conclude by considering the control issues associated with the construction of hybrid CBR systems. We review the requirements of such systems and consider how well the two existing cooperation architectures match those requirements.

Adcuctive Reasoning with Abstraction Axioms

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 how explanations can be computed according to the definition.

Generating Tests using Abduction

Abstract: Suppose we are given a theory of system behavior and a set of candidate hypotheses. Our concern is with generating tests which will discriminate these hypotheses in some fashion. We logically characterize test generation as abductive reasoning. Aside from defining the theoretical principles underlying test generation, we are able to bring to bear the abundant research on abduction to show how test generation can be embodied in working systems. Furthermore, we address the issue of computational complexity. It has long been known that test generation is NP-complete. This is consistent with complexity results on the generation of abductive explanations. By syntactically restricting the description of our theory of system behavior or by limiting the completeness of our abductive reasoning, we are able to gain insight into tractable test generation problems.

Case-Based, Model-Based, and Explanation-Based Styles of Reasoning in Foreign Policy

Abstract: This article uses two types of experiments to examine the kinds of reasoning people use when thinking about foreign policy. Case-based, explanation-based, and model-based reasoning are offered as an appropriate taxonomy of reasoning styles, and laboratory experiments are the vehicle for empirical analysis. The first experiment uses a thought checking methodology. When combining over all subjects and scenarios in that experiment, explanation-based reasoning emerges as dominant, with the other two occurring with roughly equal probability. Case-based reasoning comes in second for general life scenarios and model-based reasoning comes in second for the international politics scenarios. The dominant role of explanation-based reasoning becomes even stronger for more expert respondents (graduate students in political science), and is not significantly diminished for respondents trained in the case method of instruction. The predominance of explanation-based thoughts over case-based and model-based thoughts is replicated, and even accentuated, in a second experiment involving a protocol analysis of unconstrained thoughts. The results of both types of experiments indicate that scholars concentrating on decision making would be remiss to represent reasoning processes as exclusively case or model or even explanation based. Reasoning in the area of foreign policy seems to be slightly more explanation based, but exhibits characteristics of each of the three modes of reasoning.

The Logic Of Only Knowing As A Unified Framework For Non-Monotonic Reasoning

Abstract: We propose to use the logic of only knowing (OL) by Levesque [10] as a unified framework that encompasses various non-monotonic formalisms and logic programming. OL is a modal logic which can be used to formalize an agent's introspective reasoning and to answer epistemic queries to databases. The OL logic allows one to formally express the statement “α is all I know” (in symbols, Oα) and to perform inferencing based on only-knowing, which is very useful for commonsense reasoning. Another nice thing about the OL logic is that it has a clear model-theoretic semantics and a simple proof theory, which is sound for the quantificational case, and both sound and complete for the prepositional case. We establish the relations between OL and various non-monotonic logics (such as default logic, circumscription) and logic programming, thus extending the existing works relating the OL logic with other non-monotonic reasoning formalisms (e.g., Levesque showed that autoepistemic logic can be embeded in OL). This is accomplished by finding the connection between OL and MBNF, the logic of Minimal Belief and Negation as Failure proposed by Lifschitz [12, 13], which is known to have close relationship with logic programming and other non-monotonic logics. Our results show that OL can be used as a unified framework to compare different non-monotonic formalisms based on the same domain.

Query expansion in an abductive information retrieval system

Abstract: The problem of automatic query expansion is studied in the context of a logic-based information retrieval system that employs - in contrast to approaches based on deductive reasoning - an abductive inference engine. Given a query, the abduction process yields a set of possible expansions to the query. An architecture for an interactive retrieval system based on abduction is proposed comprising a schema-level representation of the documents' contents and structure, an abductive retrieval engine, and a user interface which allows to control the inference process. The retrieval engine was tested on a collection of SGML-structured texts. We report on experimental results in the last section of the paper.

Nonstandard Queries and Nonstandard Answers

Abstract: An overview of cooperative answering Cobase: a cooperative database system Exploiting user models to avoid misconstruals Modal logics for practical reasoning Deriving answers to safety queries Abductive reasoning in three-valued logic for knowledge bases Labelled abduction and relevance reasoning Hypothetical reasoning with intuitionistic logic A modal analysis for subjunctive queries Updates and subjunctive queries

Abductive analysis of modular logic programs

Abstract: We introduce a practical method for abductive analysis of modular logic programs. This is obtained by reversing the deduction process, which is usually applied in static-dataflow analysis of logic programs, on generic, possibly abstract, domains for analysis. The approach is validated in the framework of abstract interpretation. The abduced information provides an abstract specification for program modules which can be of assistance both in top-down development of programs and in compile-time optimization. To the best of our knowledge this is the first application of abductive reasoning in dataflow analysis of logic programs.

ABDUCTION and EXPLANATION-BASED LEARNING: CASE STUDIES IN DIVERSE DOMAINS

Abstract: This paper presents a knowledge-based learning method and reports on case studies in different domains. The method integrates abduction and explanation-based learning. Abduction provides an improved method for constructing explanations. The improvement enlarges the set of examples that can be explained so that one can learn from additional examples using traditional explanation-based macro learning. Abduction also provides a form of knowledge level learning. Descriptions of case studies show how to set up abduction engines for tasks in particular domains. The case studies involve over a hundred examples taken from diverse domains requiring logical, physical, and psychological knowledge and reasoning. The case studies are relevant to a wide range of practical tasks including natural language understanding and plan recognition; qualitative physical reasoning and postdiction; diagnosis and signal interpretation; and decision making under uncertainty. The descriptions of the case studies include an example, its explanation, and discussions of what is learned by macro-learning and by abductive inference. The paper discusses how to provide and represent the domain knowledge and meta-knowledge needed for abduction and search control. The main conclusion is that abductive inference is important for learning. Abduction and macro-learning are complementary and synergistic.

Thinking takes time: a modal active-logic for reasoning IN time

Abstract: Most commonsense reasoning formalisms do not account for the passage of time as the reasoning occurs, and hence are inadequate from the point of view of modeling an agent’s ongoing process of reasoning. We present a modal active-logic that treats time as a valuable resource that is consumed in each step of the agent’s reasoning. We provide a sound and complete characterization for this logic and exarnine how it addresses the problem of logical omniscience.

Reasoning from Evidence: Inductive Logic

Abstract: A rotationally symmetric component and method of producing it, for optical imaging systems having a quasi-aspherical concave surface obtained as a result of cementing to a base lens a second lens, then grinding the combined lenses so that only an annular rim lens is left of the second lens, and optionally cementing thereto a third lens, and/or a fourth lens, while each time grinding and polishing the composite body to reduce the additional lenses to rim lenses, the radii and optical characteristics being of such a magnitude that the combined refractive characteristics resemble those of an aspherical surface.

Apparatus and method for data processing and/or for control

Abstract: A computer implemented abductive reasoner checks for contradictions in the reasoning operations during processing and determines subsequent processing dependent upon the results of the checking. Constraints on variables may be imposed to permit proofs to be pursued which otherwise would involve contradictions. Different types of abductive reasoning step may be assigned different costs and proofs or future processing steps selected dependent upon the costing. Alternatively or in addition, reasoning operations and the resulting proofs, if any, may be grouped according to the types of reasoning steps involved and provision may be included for pursuing operations involving less costly reasoning steps in preference or prior to operations which may be more costly. Where successive goals are to be proved, the proof of each successive goal may be performed by reference to hypotheses established in proofs of previous goals. In applications involving the production of control signals, commands defining required control signals may be provided in the form of premises in rules contained in a knowledge base with which the abducer operates and means may be provided for outputting said commands and converting them to the required control signals.

Information Flow Controls vs Interference Controls: An Integrated Approach

Abstract: This paper proposes a formal method for modeling database security based on a logical interpretation of two problems: the (internal) information flow controls and the (external) information inference controls. Examples are developed that illustrate the inability of “classical” security models such as non-interference and non-deducibility to completely take into account the inference problem, because both are too constraining: the former model leads to the existence problem, whereas the latter one leads to the elimination problem. The causality model, which has been developed to solve the information flow control problem by considering that “what is known, must be permitted to be known”, does not also explicitly take into account the inference problem. But we show that it is possible to extend causality so that inference can in fact be solved by formalizing the security policy consistency in the following way “any information must not be both permitted and forbidden, to be known”. However, some difficulties remain if we do not consider that a subject can perform not only valid derivations but also plausible derivations. In particular, we show that classical solutions to the inference problem such as use of polyinstantiated databases are not plainly satisfactory, unless the security policy is able to estimate how it is plausible that an abductive reasoning can occur.

Knowledge-based techniques in acoustic-phonetic decoding of speech: interest and limitations

Abstract: A major step in the process of speech understanding is the acoustic-phonetic decoding which can be defined as the automatic mapping of the continuous speech wave into a set of predetermined linguistic units such as phones, diphones, syllables, etc. This paper relates to the approach of this problem which consists in exploiting an explicit description of all kinds of available knowledge about the speech communication phenomena, in the general framework of an artificial intelligence knowledge-based system. We will first recall the main difficulties of acoustic-phonetic decoding with a practical example. We will then present the APHODEX system that we have been designing for the past eight years, in terms of software architecture and of knowledge representation and reasoning. The practical evaluation of this system will then be carried out at the different levels of feature extraction, segmentation and labelling. Finally, we will discuss the limitations of our approach and present the ongoing effort to overcome these limitations, especially through the use of abductive reasoning.

Abduction in Temporal Reasoning

Abstract: Commonsense knowledge often omits the temporal incidence of facts, and even the ordering between occurrences is only available for some of their instances Reasoning about the temporal extent of facts and their sequencing becomes complex due to this inherent partiality The generation of hypotheses is adopted here as a natural way to overcome the difficulties in computing answers to temporal queries The proposed abductive system performs temporal reasoning in a logic programming framework Queries are taken as goals and the inference system combines deduction with abduction and constraint solving The convenience of constraints for dealing with temporal information is widely recognized, their interest being twofold: the representation of essential properties of time and the provision for partial information, allowing flexible bounds on times instead of constant bindings Inference manipulates a language associating propositions with time periods which are maximal intervals for the proposition The abductive inference procedure is described here, identifying the constraint operations required It is also shown that the outcome of a derivation is always consistent with the information in the knowledge base

Peirce: a tool for abductive inference

Abstract: Abduction, or inference to the best explanation, has been used in artificial intelligence as a framework for solving problems ranging from diagnosis, to test interpretation, to theory formation, to natural language understanding, to perception. Previous research on computational models of abduction has suggested that a single generic strategy may be used to perform abductions in a variety of domains and across very diverse problems. This paper describes a generic strategy for abduction and a tool, Peirce, for constructing abductive problem solving systems. >

Knowledge Representation and Reasoning Under Uncertainty: Logic at Work

Abstract: The role of expectations in reasoning.- On logics of approximate reasoning.- Gentzen sequent calculus for possibilistic reasoning.- A model of inductive reasoning.- Automated reasoning with uncertainties.- An axiomatic approach to systems of prior distributions in inexact reasoning.- Contradiction removal semantics with explicit negation.- Logic programming for non-monotonic reasoning.- Agent oriented programming: An overview of the framework and summary of recent research.- An application of temporal logic for representation and reasoning about design.- Knowledge theoretic properties of topological spaces.- Rough logic for multi-agent systems.- A logical approach to multi-sources reasoning.- Situation theory and social structure.