Anticipation of goals in automated planning

被引:0
作者
Fuentetaja, Raquel [1 ]
Borrajo, Daniel [1 ]
de la Rosa, Tomas [1 ]
机构
[1] Univ Carlos III Madrid, Dept Informat, Av Univ 30, Madrid, Spain
关键词
Goal reasoning; Automated Planning; AP; Artificial Intelligence; AI;
D O I
10.3233/AIC-180753
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In the context of deliberative reasoning, autonomous systems should be able to explicitly reason about goals. Most research in automated planning assume that goals are initially given and fixed and planners generate plans to achieve them. However, in some real-world scenarios where planners work in an on-line continual planning setting, additional goals may arrive over time. When information about future goal arrivals is known, it can be exploited to direct the system towards those goals even though they have not arrived yet. Recent work presented an approach that exhibited such anticipatory behavior based on hindsight optimization on a domain-dependent setup. In this paper, we tackle this problem from the point of view of domain-independent planning. The available per-step time in on-line continual problems can be very short and domain-independent planners can scale poorly in such short time. In fact, a domain-independent reimplementation of the hindsight optimization scheme may not even be applicable. We propose several alternative approaches that consider future goals in a domain-independent planning process. Experimental results in several benchmark domains suggest that these approaches exhibit a more efficient and effective behavior than a reactive approach in which goals are pursued after their arrival.
引用
收藏
页码:117 / 135
页数:19
相关论文
共 19 条
  • [1] Bellman R., 1965, Dynamic programming and modern control theory
  • [2] Anytime heuristic search for partial satisfaction planning
    Benton, J.
    Do, Minh
    Kambhampati, Subbarao
    [J]. ARTIFICIAL INTELLIGENCE, 2009, 173 (5-6) : 562 - 592
  • [3] Benton J., 2012, ICAPS
  • [4] Burns E., 2012, P INT C AUT PLANN SC, V22, P333, DOI DOI 10.1609/ICAPS.V22I1.13533
  • [5] Chang HS, 2013, COMMUN CONTROL ENG, P1, DOI 10.1007/978-1-4471-5022-0
  • [6] Approximate receding horizon approach for Markov decision processes: average reward case
    Chang, HS
    Marcus, SI
    [J]. JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS, 2003, 286 (02) : 636 - 651
  • [7] Chong EKP, 2000, IEEE DECIS CONTR P, P1433, DOI 10.1109/CDC.2000.912059
  • [8] PDDL2.1: An extension to PDDL for expressing temporal planning domains
    Fox, M
    Long, D
    [J]. JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 2003, 20 : 61 - 124
  • [9] Geisser F, 2015, PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), P1573
  • [10] An approach to temporal planning and scheduling in domains with predictable exogenous events
    Gerevini, A
    Saetti, A
    Serina, I
    [J]. JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 2006, 25 (187-231) : 187 - 231