Showing papers on "Adaptive reasoning published in 1994"

Case-based reasoning: foundational issues, methodological variations, and system approaches

Abstract: Case-based reasoning is a recent approach to problem solving and learning that has got a lot of attention over the last few years. Originating in the US, the basic idea and underlying theories have spread to other continents, and we are now within a period of highly active research in case-based reasoning in Europe, as well. This paper gives an overview of the foundational issues related to case-based reasoning, describes some of the leading methodological approaches within the field, and exemplifies the current state through pointers to some systems. Initially, a general framework is defined, to which the subsequent descriptions and discussions will refer. The framework is influenced by recent methodologies for knowledge level descriptions of intelligent systems. The methods for case retrieval, reuse, solution testing, and learning are summarized, and their actual realization is discussed in the light of a few example systems that represent different CBR approaches. We also discuss the role of case-based methods as one type of reasoning and learning method within an integrated system architecture.

Thinking and Reasoning

Abstract: Preface. Acknowledgements. 1. Historical Background. 2. The Building Blocks of Thought. 3. Language and Thought. 4. Logic. 5. Deductive Reasoning. 6. Syllogistic Reasoning.7. Induction. 8. Hypothesis Testing. 9. Statistical Reasoning. 10. Decision Making.11. Problem Solving. 12. Game Playing and Expertise. 13. Creativity14. Everyday Reasoning. 15. Teaching Thinking. 16. The Development of Thinking: Piagetian and Information-Processing Approaches. 17. The Development of Thinking: Scientific and Conceptual Thought. 18. A Framework for the Study of Thinking. References. Name Index. Subject Index.

Problem Finding, Problem Solving, and Creativity

Abstract: Contributors Preface Problem Construction and Cognition: Applying Problem Representations in Ill-Defined Domains Problem Finding, Evaluative Thinking, and Creativity Metacognition in Creative Problem Solving Problem Finding and Empathy in Art A Method for Investigating the Creative Thought Process Problem Finding Revisited Creative Problem Solving in the Classroom: Contributions of Four Psychological Approaches The Ecology of Problem Finding and Teaching Problem Identification in Academic Research: A Longitudinal Case Study from Adolescence to Early Adulthood Scientific Problem Solving and Problem Finding: A Theoretical Model. Creative Problem Solving: An Overview Managing the Creative Process in Organizations Conclusions Concerning Creative Problem Finding, Problem Solving, and Creativity Author Index Subject Index

Adaptive Reasoning for Real-world Problems: A Schema-based Approach

Abstract: Contents: Preface. Introduction. Schema-Based Reasoning. Schemas. Deciding What to Work on. Taking Action. Handling Unanticipated Events. Memory for Schema-Based Reasoning. Implementations. Evaluation and Related Work. Conclusion. Appendices: A Diagnostic Session with MEDIC. Glossary of Medical Terms.

Qualitative Spatial Reasoning: Framework and Frontiers

Abstract: Spatial reasoning is a diverse topic; what might different spatial tasks have in common? One task where substantial progress has been made is qualitative spatial reasoning about motion. Unlike qualitative dynamics, purely qualitative spatial representations have not proven fruitful. Instead, a diagrammatic representation appears to be necessary. This paper begins by outlining the Metric Diagram/Place Vocab. ulary (MD/PV) model of qualitative spatial reasoning, illustrating its power with via two example systems-~FZ0B, a system which reasoned about motion, and CLOCK, a system which analysed fixed-axis mechanisms. We believe this model is applicable beyond simply reasoning about motion. We suspect that (1) some form of metric diagram is a central unifying factor in all spatial reasoning tasks and (2) for human spatial reasoning, the metric diagram is part of, or at least grounded in, our perceptual apparatus. In this spirit, we identify three other kinds of spatial reasoning tasks as research frontiers where substantial progress might also be made, and pose six challenge problems to serve as milestones. The frontiers are (I) deriving system function from concrete structural descriptions (2) representing and reasoning about spatially distributed systems and (3) explicating the role visual perception and recognition in reasoning.

A structured view of real-time problem solving

Abstract: Real-time problem solving is not only reasoning about time, it is also reasoning in time. This ability is becoming increasingly critical in systems that monitor and control complex processes in semiautonomous, ill-structured, real-world environments. Many techniques, mostly ad hoc, have been developed in both the real-time community and the AI community for solving problems within time constraints. However, a coherent, holistic picture does not exist. This article is an attempt to step back from the details and examine the entire issue of real-time problem solving from first principles. We examine the degrees of freedom available in structuring the problem space and the search process to reduce problem-solving variations and produce satisficing solutions within the time available. This structured approach aids in understanding and sorting out the relevance and utility of different real-time problem-solving techniques.

Integrating Induction and Case-Based Reasoning: Methodological Approach and First Evaluations

Abstract: We propose in this paper a general framework for integrating inductive and case-based reasoning (CBR) techniques for diagnosis tasks. We present a set of practical integrated approaches realised between the Kate-Induction decision tree builder and the Patdex case-based reasoning system. The integration is based on the deep understanding about the weak and strong points of each technology. This theoretical knowledge permits to specify the structural possibilities of a sound integration between the relevant components of each approach. We define different levels of integration called “cooperative”, “workbench” and “seamless”. They realise respectively a tight, medium and strong link between both techniques. Experimental results show the appropriateness of these integrated approaches for the treatment of noisy or unknown data.

Developing Model‐Based Reasoning

Abstract: Key elements of the structure and function of models in mathematics and science are identified. These elements are used as a basis for discussing the development of model‐based reasoning. A microgenetic study examines the beginnings of model‐based reasoning in a pair of fourth‐ and fifth‐grade children who solved several problems about chance and probability. Results are reported in the form of a cognitive model of children's problem‐solving performance. The cognitive model explains a transition in children's reasoning from tacit reliance on empirical regularity to a form of model‐based reasoning. Several factors fostering change in children's thinking are identified, including the role of notations, peer interaction, and teacher assistance. We suggest that model‐based reasoning is a slowly‐developing capability that emerges only with proper contextual and social support and that future study should be carried out in classrooms, where these forms of assistance can also be part of the object of study. Mode...

Flexible strategy learning: analogical replay of problem solving episodes

Abstract: This paper describes the integration of analogical reasoning into general problem solving as a method of learning at the strategy level to solve problems more effectively Learning occurs by the generation and replay of annotated derivational traces of problem solving episodes The problem solver is extended with the ability to examine its decision cycle and accumulate knowledge from the chains of successes and failures encountered during its search experience Instead of investing substantial effort deriving general rules of behavior to apply to individual decisions, the analogical reasoner compiles complete problem solving cases that are used to guide future similar situations Learned knowledge is flexibly applied to new problem solving situations even if only a partial match exists among problems We relate this work with other alternative strategy learning methods, and also with plan reuse We demonstrate the effectiveness of the analogical replay strategy by providing empirical results on the performance of a fully implemented system, PRODIGY/ANALOGY, accumulating and reusing a large case library in a complex problem solving domain

How Case-Based Reasoning Solves New Problems

Abstract: Case-based reasoning (CBR) captures lessons from past problem-solving experiences to critique or find solutions to new problems. Case-based reasoning systems have been used in several domains, both as a problem-solving methodology and as a cognitive model of the reasoning capabilities of the human mind. It is both a problem-solving approach and a computer aid that augments the memory of the human expert.

Using case-based reasoning for decision support

Abstract: Case-based reasoning is one of the most preferred method for problem solving and decision making in complex and dynamically changing situations. Case-based reasoning solves problems by relating some previously solved problems or experiences to a current, unsolved problem in a way that facilitates the search for an acceptable solution. In this paper we propose a computational case-based reasoning model, and investigate its feasibility to decision support. A performance comparison among two computational models, including ours, and a symbolic model is also conducted. We view our model not as a replacement technology, but rather as a complementary technology to the symbolic approaches for the purpose of supporting unstructured-oriented decisions. >

Modeling domain knowledge using explicit conceptualization

Abstract: A framework based on explicit conceptualizations complements the task-modeling approach. It supports flexible reasoning during problem solving, and lets domain knowledge be reused. >

Temporal Reasoning in REAKT: An Environment for Real-Time Knowledge-Based Systems

Abstract: Temporal representation and reasoning, as the ability of reasoning about temporal data, representing past, current and expected application states, is an important function to be accomplished by Real-Time Knowledge-Based Systems RTKBS, since these systems are usually applied in dynamic time-dependent problem domains. However, this feature is not completely nor usually addressed in current RTKBS tools. In this paper, a RTKBS architecture is presented, with special emphasis on its temporal reasoning function, which is integrated in a RTKBS environment with a multi-agent blackboard architecture. Representation and management of temporal data, representing past, current and future problem states and reasoning processes within these contexts are detailed.

Using introspective reasoning to guide index refinement in case-based reasoning

Abstract: Case-based reasoning research on indexing and retrieval focuses primarily on developing specific retrieval criteria, rather than on developing mechanisms by which such criteria can be learned as needed. This paper presents a framework for learning to refine indexing criteria by introspective reasoning. In our approach, a self-model of desired system performance is used to determine when and how to refine retrieval criteria. We describe the advantages of this approach for focusing learning on useful information even in the absenceof explicit processing failures, and support its benefits with experimental results on how an implementation of the model affects performance of a case-based planning system.

An integrated implementation of simulative, uncertain and metaphorical reasoning about mental states

Abstract: An unprecedented combination of simulative and metaphor based reasoning about beliefs is achieved in an AI system, ATT-Meta. Much mundane discourse about beliefs productively uses conceptual metaphors such as MIND AS CONTAINER and IDEAS AS INTERNAL UTTERANCES, and ATT-Meta's metaphor-based reasoning accordingly leads to crucial discourse comprehension decisions. ATT-Meta's non-metaphorical mode of belief reasoning includes simulative reasoning (SR). In ATT-Meta, metaphor-based reasoning can block and otherwise influence the course of SR.

Reasoning, Metareasoning, and the Promotion of Rationality

Abstract: Publisher Summary This chapter provides an overview of reasoning, meta-reasoning, and the promotion of rationality. Use of the term meta-reasoning, however, can mislead to think of the diverse tendencies and abilities involved as a single entity or process. The chapter mainly discusses that meta-reasoning is usefully construed as encompassing three distinct, though interrelated, aspects, and that all three are critical to human rationality and intelligence. The three aspects of meta-reasoning are (a) procedural meta-reasoning, involving the monitoring and direction of one's own reasoning; (b) conceptual meta-reasoning, involving declarative knowledge about reasoning, and (c) constructive meta-reasoning, involving the developmental reconstruction of one‘s reasoning and meta-reasoning. It is important to emphasize the interdependent relation of conceptual and procedural meta-reasoning. Knowledge about reasoning and executive control of one's own reasoning is likely to be intricately interconnected. Conceptual knowledge about reasoning in general and about one's own strengths and weaknesses as a reasoner may be critical to appropriate application of procedural meta-reasoning.

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.

Qualitative spatial reasoning about objects in motion: application to physics problem solving

Abstract: Describes an ongoing project to develop a theory of qualitative spatial reasoning which merges a simple, intuitive description of the spatial extent, relative position, and orientation of objects with existing methods for qualitative reasoning about dynamically changing worlds. We are applying our theories within a system for problem solving about the magnetic fields domain. We describe methods for integrating diagram and test input to a problem solver, methods of abstraction for modeling the spatial extents of objects, and a method for modeling spatial relations between objects through inequalities on extremal points which directly allows reasoning about the effects of translational motion. >

A model for integrated qualitative spatial and dynamic reasoning about physical systems

Abstract: Qualitative spatial reasoning has many applications in such diverse areas as natural language understanding, cognitive mapping, and reasoning about the physical world. We address problems whose solutions require integrated spatial and dynamic reasoning. In this paper, we present our spatial representation, based on the extremal points of objects, and show that this representation is useful for modeling the spatial extent, relative positions, and orientation of objects, and in reasoning about changes in spatial relations and orientation due to the translational and rotational motion of objects. Our theory has been implemented to support a magnetic fields problem solving application using the QPC and QSIM systems for qualitative modeling. The issues encountered in integrating spatial and dynamic reasoning in the context of these systems are also discussed.

Topics in Case-Based Reasoning: First European Workshop, EWCBR-93, Kaiserslautern, Germany, November 1-5, 1993. Selected Papers

Abstract: Understanding creativity: A case-based approach.- Analogical asides on case-based reasoning.- PRODILOGY/ANALOGY: Analogical reasoning in general problem solving.- A knowledge level model of knowledge-based reasoning.- Learning prediction of time series. A theoretical and empirical comparison of CBR with some other approaches.- Case-based and symbolic classification.- A Similarity metric for retrieval of cases imperfectly explained.- Similarity measures for structured representations.- A rule-based similarity measure.- An underlying memory model to support case retrieval.- Massively parallel case-based reasoning with probabilistic similarity metrics.- Similarity evaluation between observed behaviours for the prediction of processes.- Using k-d trees to improve the retrieval step in case-based reasoning.- Explanation-based similarity: A unifying approach for integrating domain knowledge into case-based reasoning for diagnosis and planning tasks.- Structural similarity as guidance in case-based design.- Retrieving adaptable cases.- Adaptation through interpolation for time-critical case-based reasoning.- Knowledge engineering requirements in derivational analogy.- Incorporating (Re)-interpretation in case-based reasoning.- PBL: Prototype-based learning algorithm.- Explanation-driven case-based reasoning.- A reflective architecture for integrated memory-based learning and reasoning.- A hybrid knowledge-based system for technical diagnosis learning and assistance.- Tuning rules by cases.- Using case-based reasoning to focus model-based diagnostic problem solving.- A logical representation for relevance criteria.- Multiple explanation patterns.- The application of case-based reasoning to the tasks of health care planning.- A priori selection of mesh densities for adaptive finite element analysis, using a case-based reasoning approach.- Integrating semantic structure and technical documentation in case-based service support Systems.- Case-based information retrieval.- Case-based reasoning for network management.- Case-based reasoning in a simulation environment for biological neural networks.- Integrated case-based building design.- Case-deliverer: Making cases relevant to the task at hand.- A first study on case-based planning in organic synthesis.

CHAPTER 7 – Problem Solving

Abstract: Publisher Summary This chapter describes problem solving as an activity that is like beauty, morality, and good art. However, science depends on precise definition coupled with accurate observation. If one wants to study reasoning scientifically, one has to define it in such a way that the variables in the definition closely mirror distinctions that are made in nature. Problem solving is almost the prototype of a higher mental activity. Psychologists have offered many theories of human problem solving. There have been claims that humans are actually intuitive logicians, limited only by their capacity to make computations inside their heads. Some theorists have also claimed that humans think either as if they are examining Venn diagrams or as if they are responding to linguistic cues in problem statements. The first step in problem solving is to determine the problem space, that is, how the nodes and links of a problem-solving graph are to be defined. The next step is to determine a strategy for moving from node to node.

Combining case-based reasoning and task-specific architectures

Abstract: Case-based reasoning recognizes the power of individual solutions, while task-specific architectures take advantage of commonalities among groups of solutions The key to combining the two lies in solving the riddle of case-based reasoning: how can uninterpreted cases be indexed for future use? >

Forming beliefs about a changing world

Abstract: The situation calculus is a popular technique for reasoning about action and change. However, its restriction to a first-order syntax and pure deductive reasoning makes it unsuitable in many contexts. In particular, we often face uncertainty, due either to lack of knowledge or to some probabilistic aspects of the world. While attempts have been made to address aspects of this problem, most notably using nonmonotonic reasoning formalisms, the general problem of uncertainty in reasoning about action has not been fully dealt with in a logical framework. In this paper we present a theory of action that extends the situation calculus to deal with uncertainty. Our framework is based on applying the random-worlds approach of [BGHK94] to a situation calculus ontology, enriched to allow the expression of probabilistic action effects. Our approach is able to solve many of the problems imposed by incomplete and probabilistic knowledge within a unified framework. In particular, we obtain a default Markov property for chains of actions, a derivation of conditional independence from irrelevance, and a simple solution to the frame problem.

Optimization of fuzzy systems by dynamic switching of reasoning methods

Abstract: The major idea of this dissertation is to use different fuzzy reasoning methods, e.g., aggregation operators and defuzzification methods, for optimization of fuzzy systems. This approach extends the known methods for optimization of fuzzy systems which are based essentially on optimization of the membership functions and rules. In terms of systems theory, the former approach is related to optimization of the structure of the system while the latter is related to optimization of the parameters of the membership functions and rules. The validity of this concept is demonstrated on a number of examples. The performance of a fuzzy system depends on which reasoning method is chosen. However, the best performing reasoning method depends significantly on the reasoning environment. Hence allowing for dynamic switching of reasoning methods in a fuzzy system as the reasoning situation changes can lead to better performance, even when the choice is only between two different reasoning methods. The purpose of this dissertation is to construct a generalized framework which dynamically changes the reasoning method to be used in a fuzzy system as the reasoning situation changes. In particular, the Dynamic Switching Fuzzy System (DSFS) model is proposed to dynamically switch and adjust among different reasoning methods. Furthermore, it is shown how parameterized reasoning methods (e.g., BADD defuzzification method) can be tuned by DSFS. Fuzzy meta-rules are used to implement such tuning. Additionally, it is shown that tuning reasoning methods during defuzzification is computationally more efficient than tuning the rules or membership functions. Finally, practical methods for automatic design and tuning of fuzzy systems are presented and applied to a complex control problem: swing-up control of a two-link robot called the Acrobot. A combination of Genetic Algorithms, Dynamic Switching Fuzzy Systems (DSFS), and Meta-Rule techniques is used to realize a high performance Meta-Rule Enhanced TSK controller for the Acrobot. These methods are integrated; they result in reduced design time and system complexity.

A Medical Expert System with Double-Layer Reasoning Model

Abstract: Introduces the following aspects of the medical expert system with a double-layer reasoning model:construction of double-layer reasoning model, deep-layer knowledge acquisition of reason-result relation analysis model, reasoning strategies and explanation function,etc. The double-layer reasoning with cause analysis reasoning of depth layer, it can surmount defects of the limitation and frailty of experienced reasoning,and the solitary of solving strategy of first generation expert system,add solving ability of system, arise depth of explanation function of system. It is a successful research and exploration of second generation expert system's design technology,and finally gives a diagnosed example of runing the medical expert system with double-layer reasoning model.

Explanation as a Primary Task in Problem Solving

Abstract: This article summarizes the author's perspective on the discussions that occurred at the Workshop on Explanation and Problem Solving held during the Thirteenth International Joint Conference on Artificial Intelligence*. Motivated by those discussions, the article argues for the promotion of expert system explanation from a secondary task, used mainly for communication, to a primary task that is tightly integrated with the domain problem solving of the expert system.