GOAL-ORIENTED ACTION PLANNING IN PARTIALLY OBSERVABLE STOCHASTIC DOMAINS

摘要：

　　Partially Observable Markov Decision Processes (POMDPs) provide a rich framework for sequential decision-making under uncertainty in stochastic domains.The paper presented a probabilistic conditional planning problem for Goal-Oriented Action Planning based on POMDP (called p-GOAP).We are interested in finding a plan such that the plan has maximal the goal satisfaction subject to the cost not exceeding the threshold in p-GOAP.During computing maximum goal satisfaction,we discuss a speed-up technique that alleviates the computational complexity by separating the algorithm into two phases:a greedy algorithm and a recursive process.Finally p-GOAP is proposed to cognitive reappraisal for deliberate emotion.

关键词： GOAP POMDP Cognitive appraisal

作者: Xiangyang Huang Cuihuan Du Yan Peng Xuren Wang Jie Liu

作者单位: College of Information Engineering,Capital Normal University,Beijing 100048,China School of Management,Capital Normal University,Beijing 100089,China

会议类型: 国际会议

会议名称: 2012 2nd IEEE International Conference on Cloud Computing and Intelligence Systems (2012年第2届IEEE云计算与智能系统国际会议(IEEE CCIS2012))

会议地点: 杭州

会议语种:英文

页码: 1847-1851

在线出版日期: 2012-10-30（万方平台首次上网日期，不代表论文的发表时间）