Symbolic Behavior-Recognition

It is important for robots to model other robots' unobserved actions, plans, goals and behaviors. However, classic plan recognition is ill-suited to modeling robotic systems, as (i) it assumes that actions are discrete, instantaneous and cannot take place in parallel; and (ii) it uses a planning operator-based representation, which differs significantly from the behavior-based controllers often used with robots---thus making it difficult to represent the reactive components of robots interactions with their environment. We present a behavior-based approach to planrecognition, in which hierarchical behaviors are used to model the observed robots. We show that our new model allows efficient, practical inference based on observations of parallel and continuous actions, and knowledge of the observed robots. We present highly efficient symbolic algorithms for answering key queries about observed robots, under conditions of lossy and lossless observations

Fast and Complete Symbolic Plan Recognition

Recent applications of plan recognition face several open challenges: (i) matching observations to the plan library is costly, especially with complex multi-featured observations; (ii) computing recognition hypotheses is expensive. We present techniques for addressing these challenges. First, we show a novel application of machine-learning decision-tree to efficiently map multi-featured observations to matching plan steps. Second, we provide efficient lazy-commitment recognition algorithms that avoid enumerating hypotheses with every observation, instead only carrying out bookkeeping incrementally. The algorithms answer queries as to the current state of the agent, as well as its history of selected states. We provide empirical results demonstrating their efficiency and capabilities

Fast and Complete Symbolic Plan Recognition: Allowing for Duration, Interleaved Execution, and Lossy Observations

It is important for agents to model other agents' unobserved plans and goals, based on their observable actions. This process of modeling others based on observations is known as plan-recognition. Plan recognition has been studied for many years. It often takes the form of matching observations of an agent's actions to a plan-library, a model of possible plans selected by the agent. However, there are several open key challenges in modern plan recognition: (i) handling lossy observations (where an observation or a component of an observation is intermittently lost); (ii) dealing with plan execution duration constraints; and (iii) interleaved plans (where an agent interrupts a plan for another, only to return to the first later). In this paper, we present efficient algorithms that address these challenges, in the context of symbolic plan recognition. The algorithms allow (i) efficient matching of (possibly lossy) observations to a plan library; (ii) efficient computation of all recognition hypotheses consistent with the observations, subject to interleaving and duration constraints