A belief-desire-intention architechture with a logic-based planner for agents in stochastic domains
Loading...
Authors
Rens, Gavin B.
Issue Date
2010-02
Type
Dissertation
Language
en
Keywords
Cognitive robotics , Intelligent agents , Partial observability , Situation calculus , Planning , POMDP , Belief-desire-intention paradigm , BDI theory , Logic , Situation calculus , Golog
Alternative Title
Abstract
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. Golog is based on a logic for reasoning about action—the situation calculus.
A POMDP planner on its own cannot cope well with dynamically changing environments
and complicated goals. This is exactly a strength of the belief-desire-intention (BDI) model:
BDI theory has been developed to design agents that can select goals intelligently, dynamically
abandon and adopt new goals, and yet commit to intentions for achieving goals. The contribution
of this research is twofold: (1) developing a relational POMDP planner for cognitive
robotics, (2) specifying a preliminary BDI architecture that can deal with stochasticity in action
and perception, by employing the planner.
Description
Citation
Rens, Gavin B. (2010) A belief-desire-intention architechture with a logic-based planner for agents in stochastic domains, University of South Africa, Pretoria, <http://hdl.handle.net/10500/3517>