Rens, GBMeyer, TA2016-06-272016-06-272015-01Rens, GB and Meyer, TA. 2015. A Hybrid POMDP-BDI Agent Architecture with Online Stochastic Planning and Desires with Changing Intensity Levels. In 7th Proceedings of the International Conference on Agents and Artificial Intelligence (ICAART), 10 - 12 January 2015, Lisbon, Portugal978-3-319-27946-6http://link.springer.com/chapter/10.1007%2F978-3-319-27947-3_1http://hdl.handle.net/10204/8579ICAART 2015, 7th International Conference on Agents and Artificial Intelligence, Lisbon, Portugal, 10-12 January 2015. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's websiteThe authors propose an agent architecture which combines Partially observable Markov decision processes (POMDPs) and the belief-desire-intention (BDI) framework have several complementary strengths. The authors propose an agent architecture, which combines these two powerful approaches to capitalize on their strengths. Their architecture introduces the notion of intensity of the desire for a goal’s achievement. We also define an update rule for goals’ desire levels. When to select a new goal to focus on is also defined. To verify that the proposed architecture works, experiments were run with an agent based on the architecture, in a domain where multiple goals must continually be achieved. The results show that (i) while the agent is pursuing goals, it can concurrently perform rewarding actions not directly related to its goals, (ii) the trade-off between goals and preferences can be set effectively and (iii) goals and preferences can be satisfied even while dealing with stochastic actions and perceptions. They believe that the proposed architecture furthers the theory of high-level autonomous agent reasoning.enPartially observable Markov decision processesPOMDPBelief-desire-intentionBDIPreferenceConference PresentationRens, G., & Meyer, T. (2015). Hybrid POMDP-BDI Agent Architecture with Online Stochastic Planning and Desires with Changing Intensity Levels. Springerlink. http://hdl.handle.net/10204/8579Rens, GB, and TA Meyer. "Hybrid POMDP-BDI Agent Architecture with Online Stochastic Planning and Desires with Changing Intensity Levels." (2015): http://hdl.handle.net/10204/8579Rens G, Meyer T, Hybrid POMDP-BDI Agent Architecture with Online Stochastic Planning and Desires with Changing Intensity Levels; Springerlink; 2015. http://hdl.handle.net/10204/8579 .TY - Conference Presentation AU - Rens, GB AU - Meyer, TA AB - The authors propose an agent architecture which combines Partially observable Markov decision processes (POMDPs) and the belief-desire-intention (BDI) framework have several complementary strengths. The authors propose an agent architecture, which combines these two powerful approaches to capitalize on their strengths. Their architecture introduces the notion of intensity of the desire for a goal’s achievement. We also define an update rule for goals’ desire levels. When to select a new goal to focus on is also defined. To verify that the proposed architecture works, experiments were run with an agent based on the architecture, in a domain where multiple goals must continually be achieved. The results show that (i) while the agent is pursuing goals, it can concurrently perform rewarding actions not directly related to its goals, (ii) the trade-off between goals and preferences can be set effectively and (iii) goals and preferences can be satisfied even while dealing with stochastic actions and perceptions. They believe that the proposed architecture furthers the theory of high-level autonomous agent reasoning. DA - 2015-01 DB - ResearchSpace DP - CSIR KW - Partially observable Markov decision processes KW - POMDP KW - Belief-desire-intention KW - BDI KW - Preference LK - https://researchspace.csir.co.za PY - 2015 SM - 978-3-319-27946-6 T1 - Hybrid POMDP-BDI Agent Architecture with Online Stochastic Planning and Desires with Changing Intensity Levels TI - Hybrid POMDP-BDI Agent Architecture with Online Stochastic Planning and Desires with Changing Intensity Levels UR - http://hdl.handle.net/10204/8579 ER -