Showing papers on "Abductive reasoning published in 2003"

Using event calculus to formalise policy specification and analysis

Abstract: As the interest in using policy-based approaches for systems management grows, it is becoming increasingly important to develop methods for performing analysis and refinement of policy specifications. Although this is an area that researchers have devoted some attention to, none of the proposed solutions address the issues of analysing specifications that combine authorisation and management policies; analysing policy specifications that contain constraints on the applicability of the policies; and performing a priori analysis of the specification that will both detect the presence of inconsistencies and explain the situations in which the conflict will occur. We present a method for transforming both policy and system behaviour specifications into a formal notation that is based on event calculus. Additionally it describes how this formalism can be used in conjunction with abductive reasoning techniques to perform a priori analysis of policy specifications for the various conflict types identified in the literature. Finally, it presents some initial thoughts on how this notation and analysis technique could be used to perform policy refinement.

Abduction, Reason, and Science: Processes of Discovery and Explanation

Abstract: What distinguishes abductive inference from other modes of inference? What distinguishes the different forms of abductive inference? These are perhaps the key questions that face the student of abduction, and the core of Lorenzo Magnani’s book is devoted to them. The book contains, among other things, a defence of several putative distinctions, and it touches on an impressively wide range of perspectives, citing much of the relevant work in artificial intelligence and cognitive science. Alas, abductive inference is curiously resistant to sharp distinctions, as I shall attempt to explain. C. S. Peirce used the term ‘abduction’ to refer to the process of reasoning to explanation:

Information integration using contextual knowledge and ontology merging

Abstract: With the advances in telecommunications, and the introduction of the Internet, information systems achieved physical connectivity, but have yet to establish logical connectivity. Lack of logical connectivity is often inviting disaster as in the case of Mars Orbiter, which was lost because one team used metric units, the other English while exchanging a critical maneuver data. In this Thesis, we focus on the two intertwined sub problems of logical connectivity, namely data extraction and data interpretation in the domain of heterogeneous information systems. The first challenge, data extraction, is about making it possible to easily exchange data among semi-structured and structured information systems. We describe the design and implementation of a general purpose, regular expression based Cameleon wrapper engine with an integrated capabilities-aware planner/optimizer/executioner. The second challenge, data interpretation, deals with the existence of heterogeneous contexts, whereby each source of information and potential receiver of that information may operate with a different context, leading to large-scale semantic heterogeneity. We extend the existing formalization of the COIN framework with new logical formalisms and features to handle larger set of heterogeneities between data sources. This extension, named Extended Context Interchange (ECOIN), is motivated by our analysis of financial information systems that indicates that there are three fundamental types of heterogeneities in data sources: contextual, ontological, and temporal. While COIN framework was able to deal with the contextual heterogeneities, ECOIN framework expands the scope to include ontological heterogeneities as well. In particular, we are able to deal with equational ontological conflicts (EOC), which refer to the heterogeneity in the way data items are calculated from other data items in terms of definitional equations. ECOIN provides a context-based solution to the EOC problem based on a novel approach that integrates abductive reasoning and symbolic equation solving techniques in a unified framework. Furthermore, we address the merging of independently built ECOIN applications, which involves merging disparate ontologies and contextual knowledge. The relationship between ECOIN and the Semantic Web is also discussed. Finally, we demonstrate the feasibility and features of our integration approach with a prototype implementation that provides mediated access to heterogeneous information systems. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)

Cooperation and Competition in ALIAS: A Logic Framework for Agents that Negotiate

Abstract: This paper presents ALIAS, an agent architecture based on intelligent logic agents, where the main form of agent reasoning is abduction. The system is particularly suited for solving problems where knowledge is incomplete, where agents may need to make reasonable hypotheses about the problem domain and other agents, and where the raised hypotheses have to be consistent for the overall set of agents. ALIAS agents are pro-active, exhibiting a goal-directed behavior, and autonomous, since each one can solve problems using its own private knowledge base. ALIAS agents are also social, because they are able to interact with other agents, in order to cooperatively solve problems. The coordination mechanisms are modeled by means of LAILA, a logic-based language which allows to express intra-agent reasoning and inter-agent coordination. As an application, we show how LAILA can be used to implement inter-agent dialogues, e.g., for negotiation. In particular, LAILA is well-suited to coordinate the process of negotiation aimed at exchanging resources between agents, thus allowing them to execute the plans to achieve their goals.

Abduction for Creative Design

Abstract: While abduction is considered crucial for design in general, this paper focuses on the role of abduction to integrate knowledge assuming that creative design can come from innovative combination of existing knowledge. Based on Schurz’s classification of abductive reasoning, the paper identifies that abduction for integrating theories can be performed by a special type of abduction called second order existential abduction. The paper then analyzes refrigerator design cases to understand how knowledge is used and shows that abduction is indeed central to design. It also discusses that knowledge structure is a key concept in abduction for integration.Copyright © 2003 by ASME

A model based reasoning approach for generating plausible crime scenarios from evidence

Abstract: Robust decision support systems (DSSs) for crime investigation are difficult to construct because of the almost infinite variation of plausible crime scenarios. Thus existing approaches avoid any explicit reasoning about crime scenarios. They focus on problems such as intelligence analysis and profiling. This paper introduces a novel model based reasoning technique that enables DSSs to automatically construct representations of crime scenarios. It achieves this by storing the component events of the scenarios instead of entire scenarios and by providing an algorithm that can instantiate and compose these component events into useful scenarios. This approach is more adaptable to unanticipated cases than one that represents scenarios explicitly because it allows component events to match the case under investigation in many different ways. The approach presented herein is applied to and illustrated with examples from an application of the differentiation between homicidal, suicidal, accidental and natural death.

Universal abduction studio - proposal of a design support environment for creative thinking in design

Abstract: This paper describes a new design support system that supports conceptual or creative design by dynamically integrating knowledge in different design domains We argue that abduction for integrating theories can be a basic principle to formalize such design processes Based on this principle, we propose Universal Abduction Studio, a design environment in which designers combine different theories to arrive at better design In this new approach to computational support of conceptual design, the system should offer various types of abductive reasoning from which designers can select an interesting design method We also discuss technologies to implement UAS and in this paper we propose to use analogical reasoning as abductive reasoning to discover relationships between knowledge from different sources We demonstrate that the system can discover a new idea in a design example taken from a real design activity

Financial Information Integration in the Presence of Equational Ontological Conflicts

Abstract: While there are efforts to establish a single international accounting standard, there are strong current and future needs to handle heterogeneous accounting methods and systems. We advocate a context-based approach to dealing with multiple accounting standards and equational ontological conflicts. In this paper we first define what we mean by equational ontological conflicts and then describe a new approach, using Constraint Logic Programming and abductive reasoning, to reconcile such conflicts among disparate information systems. In particular, we focus on the use of Constraint Handling Rules as a simultaneous symbolic equation solver, which is a powerful way to combine, invert and simplify multiple conversion functions that translate between different contexts. Finally, we demonstrate a sample application using our prototype implementation that demonstrates the viability of our approach

Combining abductive reasoning and inductive learning to evolve requirements specifications

Abstract: The development of requirements specifications inevitably involves modification and evolution. To support modification while preserving particular requirements goals and properties, the use of a cycle composed of two phases: analysis and revision is proposed. In the analysis phase, a desirable property of the system is checked against a partial specification. Should the property be violated, diagnostic information is provided. In the revision phase, the diagnostic information is used to help modify the specification in such a way that the new specification no longer violates the original property. An instance of the above analysis–revision cycle that combines new techniques of logical abduction and inductive learning to analyse and revise specifications, respectively is investigated. More specifically, given an (event-based) system description and a system property, abductive reasoning is applied in refutation mode to verify whether the description satisfies the property and, if it does not, identify diagnostic information in the form of a set of examples of property violation. These (counter) examples are then used to generate a corresponding set of examples of system behaviours that should be covered by the system description. Finally, such examples are used as training examples for inductive learning, changing the system description in order to resolve the property violation. This is accomplished with the use of the connectionist inductive learning and logic programming system—a hybrid system based on neural networks and the backpropagation learning algorithm. A case study of an automobile cruise control system illustrates the approach and provides some early validation of its capabilities.

Mathematical reasoning ability

Abstract: The most fundamental features of the human being is capable of thinking. Reasoning is a process to reach a conclusion by taking all related factors into account. Individuals who have reasoning ability on a subject are knowledgeable on the related discipline and can analyze new situation which are faced in all aspects, explore, make logical assumptions, explain his thoughts, reach conclusions and defense his conclusions. In this paper the following questions will be discussed. What are the mathematical reasoning approaches? How individuals’ mathematical reasoning approach does change? Is cultural differences effect change of reasoning styles? Whether individuals have certain reasoning styles or which reasoning approaches they will use according to the situation? How can people find the most suitable reasoning styles for themselves?

Reasoning about actions for e-service composition

Abstract: Composition of e-Services is the issue of synthesizing a new composite e-Service, obtained by combining a set of available component e-Services, when a client request cannot be satisfied by available e-Services. In this paper we study the problem of composition synthesis in a general framework. We consider e-Services as arbitrary (possibly infinite) execution trees, i.e., as trees of all potential interactions with clients, and characterize composition in this abstract setting. We then show how this setting can be realized using Reasoning About Actions, in particular reasoning in Situation Calculus, and exploiting a correspondence with Deterministic Propositional Dynamic Logic, we provide automated procedures and complexity results for performing composition.

Financial Information Integration In the Presence of Equational Ontological Conflicts

Abstract: While there are efforts to establish a single international accounting standard, there are strong current and future needs to handle heterogeneous accounting methods and systems. We advocate a context-based approach to dealing with multiple accounting standards and equational ontological conflicts. In this paper we first define what we mean by equational ontological conflicts and then describe a new approach, using Constraint Logic Programming and abductive reasoning, to reconcile such conflicts among disparate information systems. In particular, we focus on the use of Constraint Handling Rules as a simultaneous symbolic equation solver, which is a powerful way to combine, invert and simplify multiple conversion functions that translate between different contexts. Finally, we demonstrate a sample application using our prototype implementation that demonstrates the viability of our approach.

Abductive reasoning with recurrent neural networks

Abstract: Abduction is the process of proceeding from data describing a set of observations or events, to a set of hypotheses which best explains or accounts for the data. Cost-based abduction (CBA) is a formalism in which evidence to be explained is treated as a goal to be proven, proofs have costs based on how much needs to be assumed to complete the proof, and the set of assumptions needed to complete the least-cost proof are taken as the best explanation for the given evidence. In previous work, we presented a method for using high order recurrent networks to find least cost proofs for CBA instances. Here, we present a method that significantly reduces the size of the neural network that is produced for a given CBA instance. We present experimental results describing the performance of this method and comparing its performance to that of the previous method.

A logic for causal reasoning

Abstract: We introduce a logical formalism of irreflexive casual production relations that possesses both a standard monotonic semantics, and a natural nonmonotonic semantics. The formalism is shown to provide a complete characterization for the casual reasoning behind casual theories from [McCain and Turner, 1997]. It is shown also that any causal relation is reducible to its Horn sub-relation with respect to the nonmonotonic semantics. We describe also a general correspondence between casual relations and abductive systems, which shows, in effect, that casual relations allow to express abductive reasoning. The results of the study seem to suggest causal production relations as a viable general framework for nonmonotonic reasoning.

Abductive Logic Programs with Penalization: Semantics, Complexity and Implementation

Abstract: Abduction, first proposed in the setting of classical logics, has been studied with growing interest in the logic programming area during the last years. In this paper we study abduction with penalization in the logic programming framework. This form of abductive reasoning, which has not been previously analyzed in logic programming, turns out to represent several relevant problems, including optimization problems, very naturally. We define a formal model for abduction with penalization over logic programs, which extends the abductive framework proposed by Kakas and Mancarella. We address knowledge representation issues, encoding a number of problems in our abductive framework. In particular, we consider some relevant problems, taken from different domains, ranging from optimization theory to diagnosis and planning; their encodings turn out to be simple and elegant in our formalism. We thoroughly analyze the computational complexity of the main problems arising in the context of abduction with penalization from logic programs. Finally, we implement a system supporting the proposed abductive framework on top of the DLV engine. To this end, we design a translation from abduction problems with penalties into logic programs with weak constraints. We prove that this approach is sound and complete.

The Role of Prior Belief in Reasoning.

Abstract: In this chapter we examine research that uses well-defined laboratory problems requiring hypothetical thinking and reasoning for their solution. By “well-defined” we mean that all information required to solve the problem according to the instructions is explicitly presented. For this reason, psychologists have traditionally regarded any influence of prior knowledge or belief about the problem content or context to be normatively irrelevant to the definition of a correct answer. Consequently, where such beliefs exert an influence this has often been termed a “bias” by the investigators concerned. The effects of prior belief, however, turn out to be so pervasive in these studies that reasoning researchers in the past decade or so have begun radically to reexamine their assumptions about the nature of rational reasoning. This reassessment has been no where more visible than in the study of deductive reasoning, one of the major paradigms in this field. Typical experiments involve presenting participants with the premises of logical arguments and asking them to evaluate a conclusion presented, or draw one of their own (for reviews, see Evans, Newstead, & Byrne, 1993; Manktelow, 1999). The deduction paradigm has its origins in logicism – the belief that logic provides the rational basis for human reasoning (Evans, 2000a). The modern study of deductive reasoning dates from the 1960s, where it was motivated by the writings of psychologists such as Henle (1962) and especially Jean Piaget (Inhelder & Piaget, 1958), who proposed that adult human reasoning was inherently logical.

Abduction and Analogy in Chance Discovery

Abstract: In this chapter, we first introduce abduction and analogy as a discovery reasoning. Second, we show a hypothetical reasoning system, Theorist, as an example of computational abductive reasoning. This hypothetical reasoning system can be applied to explanatory reasoning such as design and diagnosis. However, it can not generate new hypotheses. In our explanation of hypothetical reasoning, we also show the possibilities and the limitations of conventional abduction when we use it in the context of chance discovery. Third, we show abductive analogical reasoning (AAR), which can generate new hypotheses. AAR is an extension of hypothetical reasoning that is achieved by combining abduction and analogical mapping. Finally, we show AAR as a tool for chance discovery and explain the roles of abduction and analogy in chance discovery.

Nonmonotonicity and Human Probabilistic Reasoning

Abstract: Nonmonotonic logics allow—contrary to classical (monotone) logics— for withdrawing conclusions in the light of new evidence. Nonmonotonic reasoning is often claimed to mimic human common sense reasoning. Only a few studies, though, have investigated this claim empirically. system p is a central, broadly accepted nonmonotonic reasoning system that proposes basic rationality postulates. We previously investigated empirically a probabilistic interpretation of three selected rules of system p. We found a relatively good agreement of human reasoning and principles of nonmonotonic reasoning according to the coherence interpretation of system p. This study reports an experiment on the cautious monotonicity Rule and its “incautious” counterpart that is not contained in system p, namely the monotonicity Rule. In accordance with our previous results, the data suggest that people reason nonmonotonically: the subjects in the cautious monotonicity condition infer significantly tighter intervals close to the coherence interpretation of system p compared with the subjects in the incautious monotonicity condition where rather wide (and hence non-informative) intervals are inferred.

Deductive and inductive reasoning for processing the claims of unsatisfied customers

Abstract: We report on the novel approach to modeling a dynamic domain with limited knowledge. A domain may include participating agents such that we are uncertain about motivations and decision-making principles of some of these agents. Our reasoning setting for such domains includes deductive and inductive components. The former component is based on situation calculus and describes the behavior of agents with complete information. The latter, machine learning-based inductive component (with the elements of abductive and analogous reasoning) involves the previous experience with the agent, whose actions are uncertain to the system. Suggested reasoning machinery is applied to the problem of processing the claims of unsatisfied customers. The task is to predict the future actions of a participating agent (the company that has upset the customer) to determine the required course of actions to settle down the claim. We believe our framework reflects the general situation of reasoning in dynamic domains in the conditions of uncertainty, merging analytical and analogy-based reasoning.

Explanatory coherence and empirical adequacy: The problem of abduction, and the justification of evolutionary models

Abstract: Foundationalist theories of justification for science were undermined by the theory-ladeness thesis, which has affinities with coherentist epistemologies. A challenge for defenders of coherentist theories of scientific justification is to specify coherence relations relevant to science and to show how these relations make the truth of their bearers likely. Coherence relations include characteristics that pick out better explanations in the implementation of abductive arguments. Empiricist philosophers have attacked abductive reasoning by claiming that explanatory virtues are pragmatic, having no implications regarding truth. However, empiricist's basic beliefs are subject to the same challenges facing abduction, both of which can be met by citing causally coherent etiologies, which are commonplace in biological explanations, and by demonstrating the relevance of causal coherence to truth.

Knowledge Deployment: How to Use Design Knowledge

Abstract: The research has two backgrounds. One is the recognition that design can be regarded a largely knowledge-based activity. Historically, research in design knowledge began with knowledge representation and reasoning. Design knowledge representation looked at knowledge about design objects and design processes. While the former is obvious, design process knowledge is all about how to design and is related to design reasoning that deals with how to use design object knowledge. In contrast, design reasoning was mostly concerned about how to computationally arrive at conclusions, but not how to use which knowledge, which is the central question of knowledge deployment. As theories and technologies to deal with knowledge made progresses, research focuses gradually extended to such topics as modeling, acquisition and capturing, learning, discovery, data mining, maintenance, management, reuse and sharing. These addressed, however, little about how to make knowledge well ready for use.

Creativity and self-organization: contributions from cognitive science and semiotics

Abstract: What sort of contribution has Cognitive Science to offer to the understanding of creativity? Is it appropriate to investigate creative processes from a mechanistic perspective or do they involve subjective elements which cannot - in principle - be investigated from such a perspective? These two basic questions will guide this paper which investigates creativity focusing on the nature of abductive reasoning. As an initial hypothesis we characterize creativity as a self-organizing process in which abductive reasoning occurs allowing the expansion of well structured set of beliefs. This process is considered a part of the establishment of order parameters in the flow of information available to self-organizing systems. In this sense, we argue that a deeper understanding of how self-organizing processes involving abductive reasoning may take place in dynamic systems could assist Cognitive Science in its study of creativity.

The Quality of Instructional Materials for Argumentative Knowledge Construction

Abstract: Within this paper, instructional materials for supporting argumentative knowledge construction are evaluated. Argumentative knowledge construction concerns the building of knowledge structures based on reasoning processes. Using the qualitative method of global evaluation, five books and five teaching software products were analyzed. As basis for the evaluation, six principles of good instruction were used. Theses principles concern reflective learning, multiple learning support, orientation on strengths, efficient learning, and interest. Results show strong deficits of the analyzed teaching materials in respect to effective learning-related and motivational support. Finally, suggestions for a theory-based and multi-criteria enhancement of cognitive, motivational, and emotional learning-relevant processes are made. ********** An "argument" is something that is used as a proof or as an affirmation for a statement. Knowing how to argue or reason is an important aim of education in general for a long time and is prominently anchored within curricula. It presupposes that learners can build knowledge by arguing what is known as "argumentative knowledge construction" (see, for example, Leitao, 2001). Argumentative knowledge construction concerns the process within which learners identify arguments, analyze them, consider external circumstances (e.g., use of language), reason scientifically, and apply logic. Identifying components of arguments concerns issues, premises, conclusions, and reasons for the conclusions. Analyzing arguments means to state implicit, unclear, or missing assumptions. Within all stages of argumentative knowledge construction, external circumstances (influences from values, authorities, or emotional language) have to be considered. Argumentative knowledge construction is also based on scientific-analytical reasoning (e.g., the research for causalities, the evaluation of statistical data and their underlying representativity). Finally, argumentative knowledge construction consists of more or less logical reasoning within which analogies and inductive/deductive reasoning are of main importance (e.g., Toplak & Stanovich, 2002). There are close connections from the concept of argumentative knowledge construction with the concepts of "critical thinking", "everyday reasoning", "informal logic" or "pragmatic reasoning" (e.g., Galloti, 1989; Shaw, 1996). Argumentative knowledge construction is also related to basic research from cognitive psychology, philosophy, and linguistics, especially with "inductive and deductive reasoning", "causal reasoning", "abductive reasoning", "Baysian reasoning", "adaptive thinking", or "intuitive judging" (e.g., Cheng & Holyoak, 1985; Gigerenzer, 2000). However, results from applied and basic research have not improved educational programs for promoting argumentative knowledge construction. Argumentative knowledge construction represents only a subject area of little importance within school, and, when implemented, it had no significant effects (see the literature reviews from McMillan, 1987 and Pithers & Soden, 2000). There are several reasons for this shortcoming: First, it must be stated, that argumentative knowledge construction represents a main component within curricula on a general level, but it is not formulated in detail as practicable prescriptions for teachers. So, teachers do, as a rule, not dispose of guidelines for their daily instruction. Implementing argumentative knowledge construction would be an additional work load for teachers, which they cannot take from reducing other subject areas. Also, teachers are not educated in argumentative knowledge construction. When argumentative knowledge construction takes place within classrooms, then in some form of diffuse discussions within open learning environments (e.g., projects), or as final part within a course without a sufficient amount of time and learning support for students (e. …

Anytime argumentative and abductive reasoning

Abstract: This article presents a new approximation method for computing arguments or explanations in the context of logic-based argumentative or abductive reasoning. The algorithm can be interrupted at any time returning the solution found so far. The quality of the approximation increases monotonically when more computational resources are available. The method is based on cost functions and returns lower and upper bounds.

An Abductive Proof Procedure Handling Active Rules

Abstract: We present a simple, though powerful extension of an abductive proof procedure proposed in the literature, the so-called KM-procedure, which allows one to properly treat more general forms of integrity constraints than those handled by the original procedure. These constraints are viewed as active rules, and their treatment allows the integration of a limited form of forward reasoning within the basic, backward reasoning framework upon which the KM-procedure is based. We first provide some background on Abductive Logic Programming and the KM-procedure and then formally present the extension, named AKM-procedure. The usefulness of the extension is shown by means of some simple examples.

Intellectual artefacts of expert systems meta-epistemology

Abstract: Essential to intelligent programs is computational epistemology. Expert systems, derive their computational epistemology from domain expertise. Construction of an expert system shell and an application requires a metaepistemology that transforms domain epistemology through a sequence of computational epistemology, shell epistemology; programming epistemology and application epistemology (as distinct from application ontology) into an expert system. This paper explains the metaepistemological method by reference to prototypical development of the eGanges shell (2002--4) that is suited to domains (such as law) with rule systems, systemic procedures and/or strategic paths (RPS). The computational epistemology of 3d logic is used as its logic reification suits object-oriented programming. Retroduction (Peirce, 1931. p.28), commonly known as abduction, used according to common expert sense, effects the transformation. The metamorphosis is achieved by a sequence of selections of what fits next, and what needs to be repositioned for better fit. In the eGanges design, a central two dimensional tributary structure called a river or rule map is selected out of the 3d logic model, to optimize cognitive value of the computational epistemology for the user interface. River ideographs are streamlined flowcharts that resemble fishbone diagrams. Extensive, dense river ideographs, may be variously nested as sub-maps, and also variously glossed to incorporate annotations of the RPS system. Gloss options include links between nodes in the same set of sub-maps and between parallel river systems. Sub-epistemologies may be required for the glosses. Functionality of the shell facilitates navigation and interrogation of the maps, as well as processing interrogation input.