Integration of Planning with Plan Recognition Using Classical Planners (Extended Abstract)

In order for robots to interact with humans in the world around them, it is important that they are not just aware of the presence of people, but also able to understand what those people are doing. In particular, interaction involves multiple agents which requires some form of coordination, and this cannot be achieved by acting blindly. The field of plan recognition (PR) studies methods for identifying an observed agent’s task or goal given her action sequence. This is often regarded as the inverse of planning which, given a set of goal conditions, aims to derive a sequence of actions that will achieve the goals when performed from a given initial state. Ram´ırez and Geffner (2009; 2010) proposed a simple transformation of PR problems into classical planning problems for which off-the-shelf software is available for quick and efficient implementations. However, there is a reliance on the observed agent’s optimality which makes this PR technique most useful as a post-processing step when some of the final actions are observed. In human-robot interaction (HRI), it is usually too late to interact once the humans are finished performing their tasks. In this paper, we describe ongoing work two extensions to make classical planning-based PR more applicable to the field of HRI. First, we introduce a modification to their algorithm that reduces the optimality bias’s effect so that long-term goals may be recognized at earlier observations. This is then followed by methods for extracting information from these predictions so that the observing agent may run a second pass of the planner to determine its own actions to perform for a fully interactive system.