The Experts below are selected from a list of 31362 Experts worldwide ranked by ideXlab platform
Robert P Goldman - One of the best experts on this subject based on the ideXlab platform.
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Plan Recognition in stories and in life
arXiv: Artificial Intelligence, 2013Co-Authors: Eugene Charniak, Robert P GoldmanAbstract:Plan Recognition does not work the same way in stories and in "real life" (people tend to jump to conclusions more in stories). We present a theory of this, for the particular case of how objects in stories (or in life) influence Plan Recognition decisions. We provide a Bayesian network formalization of a simple first-order theory of Plans, and show how a particular network parameter seems to govern the difference between "life-like" and "story-like" response. We then show why this parameter would be influenced (in the desired way) by a model of speaker (or author) topic selection which assumes that facts in stories are typically "relevant".
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a new model of Plan Recognition
arXiv: Artificial Intelligence, 2013Co-Authors: Robert P Goldman, Christopher W. Geib, Christopher A MillerAbstract: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.
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Plan Recognition dagstuhl seminar 11141
Dagstuhl Reports, 2011Co-Authors: Robert P Goldman, Christopher W. Geib, Henry Kautz, Tamim AsfourAbstract:This Dagstuhl seminar brought together researchers with a wide range of interests and backgrounds related to Plan and activity Recognition. It featured a substantial set of longer tutorials on aspects of Plan and activity Recognition, and related topics and useful methods, as a way of establishing a common vocabulary and shared basis of understanding. Building on this shared understanding, individual researchers presented talks about their work in the area. There were also panel discussions which addressed questions about how to best foster progress in the field --- specifically how to improve our ability to compare different Plan and activity Recognition algorithms --- and address the question of whether to assume rationality in the modeled agents (a question that is of great concern in many fields at this time). This report presents a summary of the talks and discussions at the seminar.
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a probabilistic Plan Recognition algorithm based on Plan tree grammars
Artificial Intelligence, 2009Co-Authors: Christopher W. Geib, Robert P GoldmanAbstract:We present the PHATT algorithm for Plan Recognition. Unlike previous approaches to Plan Recognition, PHATT is based on a model of Plan execution. We show that this clarifies several difficult issues in Plan Recognition including the execution of multiple interleaved root goals, partially ordered Plans, and failing to observe actions. We present the PHATT algorithm's theoretical basis, and an implementation based on tree structures. We also investigate the algorithm's complexity, both analytically and empirically. Finally, we present PHATT's integrated constraint reasoning for parametrized actions and temporal constraints.
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a new probabilistic Plan Recognition algorithm based on string rewriting
International Conference on Automated Planning and Scheduling, 2008Co-Authors: Christopher W. Geib, John Maraist, Robert P GoldmanAbstract:This document formalizes and discusses the implementation of a new, more efficient probabilistic Plan Recognition algorithm called Yet Another Probabilistic Plan Recognizer, (Yappr). Yappr is based on weighted model counting, building its models using string rewriting rather than tree adjunction or other tree building methods used in previous work. Since model construction is often the most computationally expensive part of such algorithms, this results in significant reductions in the algorithm's runtime.
Christopher W. Geib - One of the best experts on this subject based on the ideXlab platform.
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a new model of Plan Recognition
arXiv: Artificial Intelligence, 2013Co-Authors: Robert P Goldman, Christopher W. Geib, Christopher A MillerAbstract: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.
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Plan Recognition dagstuhl seminar 11141
Dagstuhl Reports, 2011Co-Authors: Robert P Goldman, Christopher W. Geib, Henry Kautz, Tamim AsfourAbstract:This Dagstuhl seminar brought together researchers with a wide range of interests and backgrounds related to Plan and activity Recognition. It featured a substantial set of longer tutorials on aspects of Plan and activity Recognition, and related topics and useful methods, as a way of establishing a common vocabulary and shared basis of understanding. Building on this shared understanding, individual researchers presented talks about their work in the area. There were also panel discussions which addressed questions about how to best foster progress in the field --- specifically how to improve our ability to compare different Plan and activity Recognition algorithms --- and address the question of whether to assume rationality in the modeled agents (a question that is of great concern in many fields at this time). This report presents a summary of the talks and discussions at the seminar.
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delaying commitment in Plan Recognition using combinatory categorial grammars
International Joint Conference on Artificial Intelligence, 2009Co-Authors: Christopher W. GeibAbstract:This paper presents a new algorithm for Plan Recognition called ELEXIR (Engine for LEXicalized Intent Recognition). ELEXIR represents the Plans to be recognized with a grammatical formalism called Combinatory Categorial Grammar(CCG). We show that representing Plans with CCGs can allow us to prevent early commitment to Plan goals and thereby reduce runtime.
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a probabilistic Plan Recognition algorithm based on Plan tree grammars
Artificial Intelligence, 2009Co-Authors: Christopher W. Geib, Robert P GoldmanAbstract:We present the PHATT algorithm for Plan Recognition. Unlike previous approaches to Plan Recognition, PHATT is based on a model of Plan execution. We show that this clarifies several difficult issues in Plan Recognition including the execution of multiple interleaved root goals, partially ordered Plans, and failing to observe actions. We present the PHATT algorithm's theoretical basis, and an implementation based on tree structures. We also investigate the algorithm's complexity, both analytically and empirically. Finally, we present PHATT's integrated constraint reasoning for parametrized actions and temporal constraints.
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a new probabilistic Plan Recognition algorithm based on string rewriting
International Conference on Automated Planning and Scheduling, 2008Co-Authors: Christopher W. Geib, John Maraist, Robert P GoldmanAbstract:This document formalizes and discusses the implementation of a new, more efficient probabilistic Plan Recognition algorithm called Yet Another Probabilistic Plan Recognizer, (Yappr). Yappr is based on weighted model counting, building its models using string rewriting rather than tree adjunction or other tree building methods used in previous work. Since model construction is often the most computationally expensive part of such algorithms, this results in significant reductions in the algorithm's runtime.
Gal A Kaminka - One of the best experts on this subject based on the ideXlab platform.
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keyhole adversarial Plan Recognition for Recognition of suspicious and anomalous behavior
Plan Activity and Intent Recognition#R##N#Theory and Practice, 2014Co-Authors: Dorit Avrahamizilberbrand, Gal A KaminkaAbstract:Abstract Adversarial Plan Recognition is the use of Plan Recognition in settings where the observed agent is an adversary, having Plans or goals that oppose those of the observer. It is one of the key application areas of Plan-Recognition techniques. There are two approaches to adversarial Plan Recognition. The first is suspicious activity Recognition; that is, directly recognizing Plans, activities, and behaviors that are known to be suspect (e.g., carrying a suitcase, then leaving it behind in a crowded area). The second is anomalous activity Recognition in which we indirectly recognize suspect behavior by first ruling out normal, nonsuspect behaviors as exPlanations for the observations. Different challenges are raised in pursuing these two approaches. In this chapter, we discuss a set of efficient Plan-Recognition algorithms that are capable of handling the variety of challenges required of realistic adversarial Plan-Recognition tasks. We describe an efficient hybrid adversarial Plan-Recognition system composed of two processes: a Plan recognizer capable of efficiently detecting anomalous behavior, and a utility-based Plan recognizer incorporating the observer’s own biases—in the form of a utility function—into the Recognition process. This allows choosing Recognition hypotheses based on their expected cost to the observer. These two components form a highly efficient adversarial Plan recognizer capable of recognizing abnormal and potentially dangerous activities. We evaluate the system with extensive experiments using real-world and simulated activity data from a variety of sources.
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monitoring teams by overhearing a multi agent Plan Recognition approach
arXiv: Artificial Intelligence, 2011Co-Authors: Gal A Kaminka, David V. Pynadath, Milind TambeAbstract:Recent years are seeing an increasing need for on-line monitoring of teams of cooperating agents, e.g., for visualization, or performance tracking. However, in monitoring deployed teams, we often cannot rely on the agents to always communicate their state to the monitoring system. This paper presents a non-intrusive approach to monitoring by 'overhearing', where the monitored team's state is inferred (via Plan-Recognition) from team-members' routine communications, exchanged as part of their coordinated task execution, and observed (overheard) by the monitoring system. Key challenges in this approach include the demanding run-time requirements of monitoring, the scarceness of observations (increasing monitoring uncertainty), and the need to scale-up monitoring to address potentially large teams. To address these, we present a set of complementary novel techniques, exploiting knowledge of the social structures and procedures in the monitored team: (i) an efficient probabilistic Plan-Recognition algorithm, well-suited for processing communications as observations; (ii) an approach to exploiting knowledge of the team's social behavior to predict future observations during execution (reducing monitoring uncertainty); and (iii) monitoring algorithms that trade expressivity for scalability, representing only certain useful monitoring hypotheses, but allowing for any number of agents and their different activities to be represented in a single coherent entity. We present an empirical evaluation of these techniques, in combination and apart, in monitoring a deployed team of agents, running on machines physically distributed across the country, and engaged in complex, dynamic task execution. We also compare the performance of these techniques to human expert and novice monitors, and show that the techniques presented are capable of monitoring at human-expert levels, despite the difficulty of the task.
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incorporating observer biases in keyhole Plan Recognition efficiently
National Conference on Artificial Intelligence, 2007Co-Authors: Dorit Avrahamizilberbrand, Gal A KaminkaAbstract:Plan Recognition is the process of inferring other agents' Plans and goals based on their observable actions. Essentially all previous work in Plan Recognition has focused on the Recognition process itself, with no regard to the use of the information in the recognizing agent. As a result, low-likelihood Recognition hypotheses that may imply significant meaning to the observer, are ignored in existing work. In this paper, we present novel efficient algorithms that allows the observer to incorporate her own biases and preferences--in the form of a utility function--into the Plan Recognition process. This allows choosing Recognition hypotheses based on their expected utility to the observer. We call this Utility-based Plan Recognition (UPR). While reasoning about such expected utilities is intractable in the general case, we present a hybrid symbolic/decision-theoretic Plan recognizer, whose complexity is O(N DT), where N is the Plan library size, D is the depth of the library and T is the number of observations. We demonstrate the efficacy of this approach with experimental results in several challenging Recognition tasks.
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fast and complete symbolic Plan Recognition
International Joint Conference on Artificial Intelligence, 2005Co-Authors: Dorit Avrahamizilberbrand, Gal A KaminkaAbstract: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.
Hankz Hankui Zhuo - One of the best experts on this subject based on the ideXlab platform.
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action model based multi agent Plan Recognition
Neural Information Processing Systems, 2012Co-Authors: Hankz Hankui Zhuo, Qiang Yang, Subbarao KambhampatiAbstract:Multi-Agent Plan Recognition (MAPR) aims to recognize dynamic team structures and team behaviors from the observed team traces (activity sequences) of a set of intelligent agents. Previous MAPR approaches required a library of team activity sequences (team Plans) be given as input. However, collecting a library of team Plans to ensure adequate coverage is often difficult and costly. In this paper, we relax this constraint, so that team Plans are not required to be provided beforehand. We assume instead that a set of action models are available. Such models are often already created to describe domain physics; i.e., the preconditions and effects of effects actions. We propose a novel approach for recognizing multi-agent team Plans based on such action models rather than libraries of team Plans. We encode the resulting MAPR problem as a satisfiability problem and solve the problem using a state-of-the-art weighted MAX-SAT solver. Our approach also allows for incompleteness in the observed Plan traces. Our empirical studies demonstrate that our algorithm is both effective and efficient in comparison to state-of-the-art MAPR methods based on Plan libraries.
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Multi-agent Plan Recognition with partial team traces and Plan libraries
IJCAI International Joint Conference on Artificial Intelligence, 2011Co-Authors: Hankz Hankui Zhuo, Lei LiAbstract:Multi-Agent Plan Recognition (MAPR) seeks to identify the dynamic team structures and team behaviors from the observed activity sequences (team traces) of a set of intelligent agents, based on a library of known team activity sequences (team Plans). Previous MAPR systems require that team traces and team Plans are fully observed. In this paper we relax this constraint, i.e., team traces and team Plans are allowed to be partial. This is an important task in applying MAPR to real-world domains, since in many applications it is often difficult to collect full team traces or team Plans due to environment limitations, e.g., military operation. This is also a hard problem since the information available is limited. We propose a novel approach to recognizing team Plans from partial team traces and team Plans. We encode the MAPR problem as a satisfaction problem and solve the problem using a state-of-the-art weighted MAX-SAT solver. We empirically show that our algorithm is both effective and efficient.
Alexander Huntemann - One of the best experts on this subject based on the ideXlab platform.
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bayesian Plan Recognition for brain computer interfaces
International Conference on Robotics and Automation, 2009Co-Authors: Eric Demeester, Alexander Huntemann, Jose Del R Millan, Hendrik Van BrusselAbstract:For people with very severe motor dysfunctions, Brain-Computer Interfaces (BCIs) may provide the solution to regain mobility and manipulation capabilities. Unfortunately, BCIs are characterized by a limited bandwidth and uncertainty on the BCI output. In the past, we have developed a Bayesian Plan Recognition framework that estimates from uncertain human-robot interface signals the task a robot should execute. This paper extends our Plan Recognition framework to incorporate uncertain BCI signals. A benchmark test is proposed and adopted to evaluate both the Plan Recognition framework and the performance of the BCI user, for the concrete application of wheelchair driving.
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Online user modeling with Gaussian Processes for Bayesian Plan Recognition during power-wheelchair steering
2008 IEEE RSJ International Conference on Intelligent Robots and Systems, 2008Co-Authors: Alexander Huntemann, Marnix Nuttin, Eric Demeester, Hendrik Van BrusselAbstract:Many elderly and disabled people experience difficulties when maneuvering an electric wheelchair. In order to make wheelchair driving a safer and more comfortable experience, there has long been the claim to equip wheelchairs with some form of intelligent controller assisting in difficult or unsafe situations. It has been observed that every user presents different symptoms causing a specific driving pattern. Therefore, if the user is to be helped and not frustrated, his/her particular driving behavior should be taken into account when assisting him/her. In this paper we present a general user modeling technique for our Bayesian framework for Plan Recognition and shared wheelchair control. Plan Recognition corresponds to estimating the Plan a user has in mind. Assistive actions can then be taken based on the estimated user Plan. A user modeling technique based on Gaussian processes has been selected, which can be adapted online to any type of driving style. The potential of Gaussian processes for user modeling is illustrated on a case study with a disabled patient suffering from spastic quadriplegia.
