The plan recognition problem: An intersection of psychology and artificial intelligence

Schmidt, Charles F.; Sridharan, N. S.; Goodson, J. L.

doi:10.1016/0004-3702(78)90012-7

Cited by 200 publications

(78 citation statements)

References 2 publications

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“…Since our formal definition is in terms of planning, automating auditing depends upon automated plan recognition [30]. We build upon works that use models of planning to recognize plans (e.g., [31][32][33][34]).…”

Section: Prior Workmentioning

confidence: 99%

Purpose Restrictions on Information Use

Tschantz

Datta

Wing

2013

Lecture Notes in Computer Science

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Abstract. Privacy policies in sectors as diverse as Web services, finance and healthcare often place restrictions on the purposes for which a governed entity may use personal information. Thus, automated methods for enforcing privacy policies require a semantics of purpose restrictions to determine whether a governed agent used information for a purpose. We provide such a semantics using a formalism based on planning. We model planning using Partially Observable Markov Decision Processes (POMDPs), which supports an explicit model of information. We argue that information use is for a purpose if and only if the information is used while planning to optimize the satisfaction of that purpose under the POMDP model. We determine information use by simulating ignorance of the information prohibited by the purpose restriction, which we relate to noninterference. We use this semantics to develop a sound audit algorithm to automate the enforcement of purpose restrictions.

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Section: Prior Workmentioning

confidence: 99%

Purpose Restrictions on Information Use

Tschantz

Datta

Wing

2013

Lecture Notes in Computer Science

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“…The The resulting clauses are then connected to inverse-AND nodes to arrive at a BN corresponding to (1). Performing this operation for all s i,t yields a measurement system for the situation model.…”

Section: Modeling Situationsmentioning

confidence: 99%

“…Intention recognition [1] is the process of estimating the force driving human actions based on noisy observations of the human's interactions with his environment. For example, a robot embedded in a household can assist the human at its best, when estimating whether the human wants to cook, wash, etc., based on observations such as his location, grasping activity, and object interactions [2].…”

Section: Introductionmentioning

confidence: 99%

Situation-Specific Intention Recognition for Human-Robot Cooperation

Krauthausen

Hanebeck

2010

KI 2010: Advances in Artificial Intelligence

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Abstract. Recognizing human intentions is part of the decision process in many technical devices. In order to achieve natural interaction, the required estimation quality and the used computation time need to be balanced. This becomes challenging, if the number of sensors is high and measurement systems are complex. In this paper, a model predictive approach to this problem based on online switching of small, situation-specific Dynamic Bayesian Networks is proposed. The contributions are an efficient modeling and inference of situations and a greedy model predictive switching algorithm maximizing the mutual information of predicted situations. The achievable accuracy and computational savings are demonstrated for a household scenario by using an extended range telepresence system.

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“…If the product fails to come close to the user's goal, the critic cannot infer that goal or might infer a goal different from the user's goal. Goal recognition is related to task-oriented parsing (Hoppe, 1988) and plan recognition, a research area in artificial intelligence (Schmidt, Sridharan & Goodson, 1978;London & Clancey, 1982;Carver, Lesser & McCue, 1984).…”

Section: Goal Acquisitionmentioning

confidence: 99%