City Research Online

SMART (Stochastic Model Acquisition with ReinforcemenT) learning agents: A preliminary report

Child, C. H. T. & Stathis, K. (2005). SMART (Stochastic Model Acquisition with ReinforcemenT) learning agents: A preliminary report. Lecture Notes in Computer Science: Adaptive Agents and Multi-Agent Systems II, 3394, pp. 73-87. doi: 10.1007/978-3-540-32274-0_5


We present a framework for building agents that learn using SMART, a system that combines stochastic model acquisition with reinforcement learning to enable an agent to model its environment through experience and subsequently form action selection policies using the acquired model. We extend an existing algorithm for automatic creation of stochastic strips operators [9] as a preliminary method of environment modelling. We then define the process of generation of future states using these operators and an initial state and finally show the process by which the agent can use the generated states to form a policy with a standard reinforcement learning algorithm. The potential of SMART is exemplified using the well-known predator prey scenario. Results of applying SMART to this environment and directions for future work are discussed.

Publication Type: Article
Publisher Keywords: Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Science & Technology > Computer Science
[thumbnail of SMART-AAMAS03.pdf]
Text - Draft Version
Download (88kB) | Preview


Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email


Downloads per month over past year

View more statistics

Actions (login required)

Admin Login Admin Login