Relevance Grounding for Planning in Relational Domains

Abstract. Probabilistic relational models are an efficient way to learn and represent the dynamics in realistic environments consisting of many objects. Autonomous intelligent agents that ground this representation for all objects need to plan in exponentially large state spaces and large sets of stochastic actions. A key insight for computational efficiency is that successful planning typically involves only a small subset of relevant objects. In this paper, we introduce a probabilistic model to represent planning with subsets of objects and provide a definition of object relevance. Our definition is sufficient to prove consistency between repeated planning in partially grounded models restricted to relevant objects and planning in the fully grounded model. We propose an algorithm that exploits object relevance to plan efficiently in complex domains. Empirical results in a simulated 3D blocksworld with an articulated manipulator and realistic physics prove the effectiveness of our approach.

A probabilistic approach to robust execution of temporal plans with uncertainty

In Temporal Planning a typical assumption is that the agent controls the execution time of all events such as starting and ending actions. In real domains however, this assumption is commonly violated and certain events are beyond the direct control of the plan’s executive. Previous work on reasoning with uncontrollable events (Simple Temporal Problem with Uncertainty) assumes that we can bound the occurrence of each uncontrollable within a time interval. In principle however, there is no such bounding interval since there is always a non-zero probability the event will occur outside the bounds. Here we develop a new more general formalism called the Probabilistic Simple Temporal Problem (PSTP) following a probabilistic approach. We present a method for scheduling a PSTP maximizing the probability of correct execution. Subsequently, we use this method to solve the problem of finding an optimal execution strategy, i.e. a dynamic schedule where scheduling decisions can be made on-line. 1