Agent-Based Defeasible Control in Dynamic Environments by John-Jules Ch. Meyer, Jan Treur PDF

By John-Jules Ch. Meyer, Jan Treur

ISBN-10: 9048161096

ISBN-13: 9789048161096

ISBN-10: 9401717419

ISBN-13: 9789401717410

This quantity, the seventh quantity within the DRUMS instruction manual sequence, is a part of the aftermath of the winning ESPRIT venture DRUMS (Defeasible Reasoning and Uncertainty administration platforms) which happened in levels from 1989- 1996. within the moment degree (1993-1996) a piece package deal was once brought dedicated to the themes Reasoning and Dynamics, overlaying either the themes of "Dynamics of Reasoning", the place reasoning is considered as a method, and "Reasoning approximately Dynamics", which has to be understood as relating how either designers of and brokers inside of dynamic structures may well cause approximately those structures. the current quantity provides paintings performed during this context prolonged with a few paintings performed through remarkable researchers open air the undertaking on similar concerns. whereas the former quantity during this sequence had its specialize in the dynamics of reasoning professional­ cesses, the current quantity is extra eager about "reasoning approximately dynamics', viz. how (human and synthetic) brokers cause approximately (systems in) dynamic environments as a way to keep watch over them. specifically we contemplate modelling frameworks and commonplace agent versions for modelling those dynamic structures and formal ways to those structures akin to logics for brokers and formal skill to cause approximately agent­ dependent and compositional structures, and motion & swap extra regularly. We take this chance to say that we have got very friendly reminiscences of the undertaking, with its vigorous workshops and different conferences, with the numerous websites and researchers concerned, either inside and out of doors our personal paintings package.

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M. JONKER AND J. TREUR Assumption Determination Based on the current state of one diagnostic process, assumptions are generated. Relevant input atoms: Relevant output atoms: rejected(H:HYPOTHESIS, S:SIGN), has_been_considered( H: HYPOTHESIS), observation_result(O:OBSERVATION, S:SIGN) poss_assumption(H:HYPOTHESIS, S:SIGN) Relevant part of the knowledge base: if observation_result( car _starts, neg) and not has_been_considered(battery_empty) then poss_assumption(battery_empty, pos) if rejected(battery_empty, pos) and not has_been_considered(sparking_plugs_problem) then poss_assu mption (sparki ng_pl ugs_problem, pos) if rejected(H:HYPOTHESIS, pos) then poss_assumption(H:HYPOTHESIS, neg) Assumption Evaluation Based on the selected assumption, the predicted and actual observation result, an evaluation is made.

5 Composition of information types The relations between the information types distinguished above and generic information types distinguished for diagnosis, are depicted below in Figures 12 and 13. The information types Selected Hypotheses, Assessed Hypotheses and Focussed Hypotheses, see Figure 12, refer to the information type Meta Domain Hypotheses. The information type Meta-Domain Hypotheses refers to the information types Hypotheses Sorts and Domain Hypotheses. Note in this respect the meta-object distinction between Meta-Domain Hypotheses and Domain Hypotheses.

An option for more fine-grained control is to COMPOSITIONAL DESIGN OF MULTI-AGENT SYSTEMS 43 dynamically generate goals (targets) for the reasoning process within a (primitive) component, thus supporting dynamic control of limited reasoning within a component. Another option for more fine-grained control is to dynamically generate additional presuppositions (assumptions) for the reasoning process in a component. In this section the control of reasoning by dynamic targets and by dynamic assumptions is discussed.

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Agent-Based Defeasible Control in Dynamic Environments by John-Jules Ch. Meyer, Jan Treur

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