TY - JOUR
T1 - Reasoning about intentions in uncertain domains
AU - Schut, Martijn
AU - Wooldridge, Michael
AU - Parsons, Simon
PY - 2001
Y1 - 2001
N2 - The design of autonomous agents that are situated in real world domains involves dealing with uncertainty in terms of dynamism, observability and non-determinism. These three types of uncertainty, when combined with the real-time requirements of many application do- mains, imply that an agent must be capable of effiectively coordinating its reasoning. As such, situated belief-desire-intention (bdi) agents need an efficient intention reconsideration policy, which defines when computational resources are spent on reasoning, i.e., deliberating over intentions, and when resources are better spent on either object level reasoning or action. This paper presents an implementation of such a policy by modelling intention reconsideration as a partially observable Markov decision process (pomdp). The motivation for a pomdp implementation of intention reconsideration is that the two processes have similar properties and functions, as we demonstrate in this paper. Our approach achieves better results than existing intention reconsideration frameworks, as is demonstrated empirically in this paper. © Springer-Verlag Berlin Heidelberg 2001.
AB - The design of autonomous agents that are situated in real world domains involves dealing with uncertainty in terms of dynamism, observability and non-determinism. These three types of uncertainty, when combined with the real-time requirements of many application do- mains, imply that an agent must be capable of effiectively coordinating its reasoning. As such, situated belief-desire-intention (bdi) agents need an efficient intention reconsideration policy, which defines when computational resources are spent on reasoning, i.e., deliberating over intentions, and when resources are better spent on either object level reasoning or action. This paper presents an implementation of such a policy by modelling intention reconsideration as a partially observable Markov decision process (pomdp). The motivation for a pomdp implementation of intention reconsideration is that the two processes have similar properties and functions, as we demonstrate in this paper. Our approach achieves better results than existing intention reconsideration frameworks, as is demonstrated empirically in this paper. © Springer-Verlag Berlin Heidelberg 2001.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=67650322431&origin=inward
U2 - https://doi.org/10.1007/3-540-44652-4_9
DO - https://doi.org/10.1007/3-540-44652-4_9
M3 - Article
VL - 2143
SP - 84
EP - 95
JO - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
JF - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
ER -