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Rational Agents and emotion

Heemskerk, M. (1999) Rational Agents and emotion. Master's Thesis / Essay, Artificial Intelligence.

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Abstract

A central issue in the development of agents that have practical reasoning skills is the concept of resource-boundedness. For an agent to reason effectively, he has to use the limited resources that are available to him (e.g. processing time, memory) in an effective way. Research on planning systems such as STRIPS [FN7I] and the limitations of these systems have led to the development of the BDI paradigm. According to this approach, an agent can be described as having beliefs, desires and intentions. Roughly speaking, beliefs correspond to the agent's knowledge of the world, desires correspond to the agent's goals, and intentions correspond to the set of goals that an agent has currently adopted. Adding the 'mental' state intention to agent ontology greatly reduces the computational costs of the agent's planning processes, making it feasible for an agent to perform in realtime. However,in principle the BDI approach cannot guarantee that an agent is responsive to its environment, another important requirement for an agent that functions in a realworld application. Practical systems based on BDI theory such as PRS [GL87] and dMARS [dLW97] have addressed this issue, but not in a conceptually convincing fashion. In humans, emotions appear to play a key role in ensuring our being responsive to the environment. In this thesis, I explore psychological and computational models of emotion, and incorporate a computational model of control, based on models of emotion, in the BDI architecture. The resulting extended BDI architecture enables agents to adapt the timing of planning processes to the current state of the environment. For example, the resulting agent will interrupt his current planning processes and re-plan his actions when he becomes 'afraid'. Finally, I have implemented a greatly simplified version of the proposed extended BDI architecture in order to test the effectiveness of the model.

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Artificial Intelligence
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 07:30
Last Modified: 15 Feb 2018 07:30
URI: http://fse.studenttheses.ub.rug.nl/id/eprint/9032

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