Representation, organization, and use of topographic models of physical spaces for route planning

Goel, Ashok K.; Callantine, Todd J.; Shankar, M. Gowri; Chandrasekaran, B.

doi:10.1109/caia.1991.120886

Cited by 17 publications

(4 citation statements)

References 11 publications

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“…3.2.6 ROUTER ROUTER (Goel & Callentine, 1992;Goel et al, 1991) is a system for path planning which uses both a case-based solver and a numerical model-based solver. The application area is route planning.…”

Section: Cabaretmentioning

confidence: 99%

Integrating multiple problem solvers in knowledge-based systems

Christodoulou

Keravnou

1997

The Knowledge Engineering Review

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Research in knowledge-based systems has shown that the use of multiple knowledge representation formalisms and reasoning mechanisms for achieving a specific task in complex domains could result in efficient and effective problem solving. This has led t o the development of a number of general architectures and application-specific systems integrating multiple problem solvers. A problem solver is defined to be an association between a knowledge intensive (sub)task, an inference mechanism and a knowledge representation formalism on which the inference mechanism is working to achieve the (sub)task. A knowledge-based system making use of different problem solvers should address a number of critical aspects of integration of the solvers, like interaction, in vocation, reactiveness, learning and expandability. The aim of this paper is to distinguish and discuss essential integration aspects, and to review a number of proposed general hybrid architectures and application specific hybrid systems on the basis of these aspects. The review shows that none of these general architectures or application specific systems directly addresses all the identified integration aspects. In general, the limitations exhibited by these systems are due to the naive form of interac tion and invocation of the integrated solvers. We give a high level specification for a competent hybrid knowledge-based architecture that supports the identified integration aspects.

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Section: Cabaretmentioning

confidence: 99%

Integrating multiple problem solvers in knowledge-based systems

Christodoulou

Keravnou

1997

The Knowledge Engineering Review

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“…For example, fig. 2a illustrates the approach used by the ROUTER system (Goel et al, 1991). In this approach to constructing a hybrid system, some control module selects whether to use the CBR system or the co-reasoner.…”

Section: Case-based Reasoningmentioning

confidence: 99%

“…This is a system for path planning which uses both a CBR system and a numerical model-based system (Goel and Callentine, 1992;Goel et al, 1991). When Router is presented with a problem it heuristically selects which reasoner to use.…”

Section: Routermentioning

confidence: 99%

Hybrid case-based reasoning

Hunt

Miles

1994

The Knowledge Engineering Review

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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.

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“…In our work, we use Router, [13], a path planning system, as a case-study problem solver. Router's task is to find a path from an initial location to a goal location in a physical space.…”

mentioning

confidence: 99%

Learning problem-solving concepts by reflecting on problem solving

Stroulia

Goel

1994

Machine Learning: ECML-94

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Abstract. Learning and problem solving are intimately related: problem solving determines the knowledge requirements of the reasoner which learning must fulfill, and learning enables improved problem-solving performance. Different models of problem solving, however, recognize different knowledge needs, and, as a result, set up different learning tasks. Some recent models analyze problem solving in terms of generic tasks, methods, and subtasks. These models require the learning of problemsolving concepts such as new tasks and new task decompositions. We view reflection as a core process for learning these problem-solving concepts. In this paper, we identify the learning issues raised by the taskstructure framework of problem solving. We view the problem solver as an abstract device~ and represent how it works in terms of a structurebehavior-function model which specifies how the knowledge and reasoning of the problem solver results in the accomplishment of its tasks. We describe how this model enables reflection, and how model-based reflection enables the reasoner to adapt its task structure to produce solutions of better quality. The Autognostic system illustrates this reflection process.

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Representation, organization, and use of topographic models of physical spaces for route planning

Cited by 17 publications

References 11 publications

Integrating multiple problem solvers in knowledge-based systems

Integrating multiple problem solvers in knowledge-based systems

Hybrid case-based reasoning

Learning problem-solving concepts by reflecting on problem solving

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