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

• 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

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. === Computing === M. Sc. (Computer Science)