Integrating multiple paradigms within the blackboard framework

Vraneš, Sanja; Stanojevic, Mladen

doi:10.1109/32.372151

Cited by 41 publications

(11 citation statements)

References 25 publications

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“…They use semantic nets (Minsky, 1968), frames (Minsky, 1975), logical formalism (Chang & Lee, 1973), or the combination of these paradigms (Vraneš & Stanojević, 1995) to describe the domain of application, and rules or predicate calculus to define inference procedures. The main characteristic of all classicist knowledge representation techniques is that they use names to determine the meaning.…”

Section: Classicist Approachmentioning

confidence: 99%

Knowledge representation with SOUL

Stanojevic

Vraneš

2007

Expert Systems with Applications

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Section: Classicist Approachmentioning

confidence: 99%

Knowledge representation with SOUL

Stanojevic

Vraneš

2007

Expert Systems with Applications

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“…16 We arrange these agents using a blackboard-based architecture 15,27 as the main paradigm for communication and control. Under this model, a series of agents share the available information about the system and the environment.…”

Section: Agents and Behaviors Definitionmentioning

confidence: 99%

A framework for defining and learning fuzzy behaviors for autonomous mobile robots

Barberá

Skármeta

2002

Int. J. Intell. Syst.

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In this paper we show our work on the use of fuzzy behaviors in the field of autonomous mobile robots. We address here how we use learning techniques to efficiently coordinate the conflicts between the different behaviors that compete with each other to take control of the robot. We use fuzzy rules to perform such fusion. These rules can be set using expert knowledge, but as this can be a complex task, we show how to automatically define them using genetic algorithms. We also describe the working environment, which includes a custom programming language (named BG) based on the multi-agent paradigm. Finally, some results related to simple goods-delivery tasks in an unknown environment are presented.

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“…The data transformation stage is a critical part of any data warehouse, since it usually determines the overall performance of the system [18,10]. It is also very important to keep scalability and extensibility in mind during the design stage, for the data warehouse is likely to require additional features and raw-power as the application processing needs grow.…”

Section: Reconfigurable Filtersmentioning

confidence: 99%