A New Model of Plan Recognition

We present a new abductive, probabilistic theory of plan recognition. This model differs from previous plan recognition theories in being centered around a model of plan execution: most previous methods have been based on plans as formal objects or on rules describing the recognition process. We show that our new model accounts for phenomena omitted from most previous plan recognition theories: notably the cumulative effect of a sequence of observations of partially-ordered, interleaved plans and the effect of context on plan adoption. The model also supports inferences about the evolution of plan execution in situations where another agent intervenes in plan execution. This facility provides support for using plan recognition to build systems that will intelligently assist a user.Comment: Appears in Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI1999

Intention Recognition for Partial-Order Plans Using Dynamic Bayesian Networks

In this paper, a novel probabilistic approach to intention recognition for partial-order plans is proposed. The key idea is to exploit independences between subplans to substantially reduce the state space sizes in the compiled Dynamic Bayesian Networks. This makes inference more efficient. The main con- tributions are the computationally exploitable definition of subplan structures, the introduction of a novel Lay- ered Intention Model and a Dynamic Bayesian Net- work representation with an inference mechanism that exploits consecutive and concurrent subplans\u27 indepen- dences. The presented approach reduces the state space to the order of the most complex subplan and requires only minor changes in the standard inference mecha- nism. The practicability of this approach is demon- strated by recognizing the process of shelf-assembly

A plan classifier based on Chi-square distribution tests

To make good decisions in a social context, humans often need to recognize the plan underlying the behavior of others, and make predictions based on this recognition. This process, when carried out by software agents or robots, is known as plan recognition, or agent modeling. Most existing techniques for plan recognition assume the availability of carefully hand-crafted plan libraries, which encode the a-priori known behavioral repertoire of the observed agents; during run-time, plan recognition algorithms match the observed behavior of the agents against the plan-libraries, and matches are reported as hypotheses. Unfortunately, techniques for automatically acquiring plan-libraries from observations, e.g., by learning or data-mining, are only beginning to emerge. We present an approach for automatically creating the model of an agent behavior based on the observation and analysis of its atomic behaviors. In this approach, observations of an agent behavior are transformed into a sequence of atomic behaviors (events). This stream is analyzed in order to get the corresponding behavior model, represented by a distribution of relevant events. Once the model has been created, the proposed approach presents a method using a statistical test for classifying an observed behavior. Therefore, in this research, the problem of behavior classification is examined as a problem of learning to characterize the behavior of an agent in terms of sequences of atomic behaviors. The experiment results of this paper show that a system based on our approach can efficiently recognize different behaviors in different domains, in particular UNIX command-line data, and RoboCup soccer simulationThis work has been partially supported by the Spanish Government under project TRA2007-67374-C02-0

Reconnaissance des buts d'un agent à partir d'une observation partielle de ses actions et des connaissances stratégiques de son espace de décision

La capacité de reconnaître les intentions des autres est une composante essentielle non seulement de l'intelligence humaine mais aussi de l'intelligence artificielle dans plusieurs domaines d'application. Pour les algorithmes d'intelligence artificielle, reconnaître l'intention d'un agent à partir d'une observation partielle de ses actions demeure un défi de taille. Par exemple dans les jeux de stratégie en temps réel, on aimerait reconnaître les intentions de son adversaire afin de mieux contrer ses actions futures. En domotique, on voudrait une maison capable de comprendre et d'anticiper les intentions de ses habitants pour maximiser leur confort et les assister dans leurs activités quotidiennes. Dans le domaine de la sécurité informatique, un outil de détection des intrus doit pouvoir observer les actions des usagers du réseau et déceler ceux qui ont des intentions malveillantes. Ce mémoire de maîtrise propose d'aborder ce problème sous observabilité partielle par adaptation des méthodes utilisées dans l'analyse grammaticale probabiliste. L'approche probabiliste considérée utilise une grammaire hors contexte de multi-ensemble partiellement ordonnée et considère la poursuite de plusieurs buts simultanément, ordonnés ou non. Cela revient donc à faire de l'analyse grammaticale probabiliste avec plusieurs symboles de départ

Predicting Plan Failure by Monitoring Action Sequences and Duration

Anticipating failures in agent plan execution is important to enable an agent to develop strategies to avoid or circumvent such failures, allowing the agent to achieve its goal.  Plan recognition can be used to infer which plans are being executed from observations of sequences of activities being performed by an agent. In this work, we use this symbolic plan recognition algorithm to find out which plan the agent is performing and develop a failure prediction system, based on plan library information and in a simplified calendar that manages the goals the agent has to achieve. This failure predictor is able to monitor the sequence of agent actions and detects if an action is taking too long or does not match the plan that the agent was expected to perform. We showcase this approach successfully in a health-care prototype system

The Meaning of Action:a review on action recognition and mapping

In this paper, we analyze the different approaches taken to date within the computer vision, robotics and artificial intelligence communities for the representation, recognition, synthesis and understanding of action. We deal with action at different levels of complexity and provide the reader with the necessary related literature references. We put the literature references further into context and outline a possible interpretation of action by taking into account the different aspects of action recognition, action synthesis and task-level planning