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Bridging Commonsense Reasoning and Probabilistic Planning via a Probabilistic Action Language

机译:通过概率动作语言桥接常识推理和概率计划

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To be responsive to dynamically changing real-world environments, an intelligent agent needs to perform complex sequential decision-making tasks that are often guided by commonsense knowledge. The previous work on this line of research led to the framework called interleaved commonsense reasoning and probabilistic planning (iCORPP), which used P-log for representing commmonsense knowledge and Markov Decision Processes (MDPs) or Partially Observable MDPs (POMDPs) for planning under uncertainty. A main limitation of icorpp is that its implementation requires non-trivial engineering efforts to bridge the commonsense reasoning and probabilistic planning formalisms. In this paper, we present a unified framework to integrate iCORPP's reasoning and planning components. In particular, we extend probabilistic action language pBC+ to express utility, belief states, and observation as in POMDP models. Inheriting the advantages of action languages, the new action language provides an elaboration tolerant representation of POMDP that reflects commonsense knowledge. The idea led to the design of the system PBCPLUS2POMDP, which compiles a pBC+ action description into a POMDP model that can be directly processed by off-the-shelf POMDP solvers to compute an optimal policy of the pBC+ action description. Our experiments show that it retains the advantages of icorpp while avoiding the manual efforts in bridging the commonsense reasoner and the probabilistic planner.
机译:为了对动态变化的现实环境做出响应,智能代理需要执行通常由常识知识指导的复杂的顺序决策任务。先前在这一研究领域的工作导致了一个被称为交错常识推理和概率计划(iCORPP)的框架,该框架使用P-log表示通信知识,并使用Markov决策过程(MDP)或部分可观察的MDP(POMDP)进行不确定性下的计划。 。 icorpp的主要局限性在于它的实现需要不懈的工程努力来弥合常识性推理和概率规划形式主义。在本文中,我们提出了一个统一的框架来集成iCORPP的推理和计划组件。特别是,我们扩展了概率动作语言pBC +来表达效用,信念状态和观察,就像在POMDP模型中一样。继承了动作语言的优点,新的动作语言提供了POMDP的精致容忍表示,反映了常识。这个想法导致了系统PBCPLUS2POMDP的设计,该系统将pBC +动作描述编译成POMDP模型,可以由现成的POMDP求解器直接处理以计算pBC +动作描述的最佳策略。我们的实验表明,它保留了icorpp的优势,同时避免了将常识推理器和概率规划器联系起来的手动工作。

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