A belief-desire-intention architechture with a logic-based planner for agents in stochastic domains

A belief-desire-intention architechture with a logic-based planner for agents in stochastic domains

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This dissertation investigates high-level decision making for agents that are both goal and utility driven. We develop a partially observable Markov decision process (POMDP) planner which is an extension of an agent programming language called DTGolog, itself an extension of the Golog language. G...

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Bibliographic Details
Main Author: Rens, Gavin B.
Other Authors: Van der Poel, E.
Format: Others
Language:en
Published: 2010
Subjects:
Cognitive robotics
Intelligent agents
Partial observability
Situation calculus
Planning
POMDP
Belief-desire-intention paradigm
BDI theory
Logic
Golog
006.3
Markov processes
Statistical decision
Dynamic programming
Robots > Control systems
Automatic control
Robotics
Online Access:http://hdl.handle.net/10500/3517

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