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Modelling Learning for Intelligent Software Agents: A Connectionist Approach

Joyce, Dan W. and Lewis, Paul H. (1999) Modelling Learning for Intelligent Software Agents: A Connectionist Approach At Proceedings of the Second Workshop on Intelligent Virtual Agents. , pp. 143-46.

Record type: Conference or Workshop Item (Other)


This paper aims to show how a connectionist model can provide a form of adaptive action selection mechanism (ASM) for reactive virtual agents. By adopting a horizontally layered control architecture, we can build an agent with the ability to learn associations between sensory input and internal state to produce and adapt predictions or responses. At the lowest level, stimuli are categorised by a plastic self-organising mechanism which then activates a prediction module. Subsequently, if the prediction module's action results in a harmful environmental consequence, a conditioning network (reflecting internal state) modifies the agent's choice of prediction during the remainder of its attempt to find the optimal action. This acquisition of behaviour is regulated by a control layer and finally, an application-specific layer.

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Published date: 1999
Venue - Dates: Proceedings of the Second Workshop on Intelligent Virtual Agents, 1999-01-01
Organisations: Web & Internet Science


Local EPrints ID: 252539
PURE UUID: 3d1fa04c-1d9f-4553-aba6-62e3250221b4

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Date deposited: 22 Feb 2000
Last modified: 18 Jul 2017 10:04

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Author: Dan W. Joyce
Author: Paul H. Lewis

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