Theories of Intentions in the Framework of Situation Calculus

Abstract. A method to recognize agent's intentions is presented in a framework that combines the logic of Situation Calculus and Probability Theory. The method is restricted to contexts where the agent only performs procedures in a given library of procedures, and where the system that intends to recognize the agent's intentions has a complete knowledge of the actions performed by the agent. An original aspect is that the procedures are defined for human agents and not for artificial agents. The consequence is that the procedures may offer the possibility to do any kind of actions between two given actions, and they also may forbid to perform some specific actions. Then, the problem is different and more complex than the standard problem of plan recognition. To select the procedures that partially match the observations we consider the procedures that have the greatest estimated probability. This estimation is based on the application of Bayes' theorem and on specific heuristics. These heuristics depend on the history and not just on the last observation. A PROLOG prototype of the presented method has been implemented.

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“…That corresponds to the notion of intention persistence proposed by Cohen and Levesque in [9] (see also [16]). …”

Section: A Simple Examplesupporting

confidence: 58%

Intention Recognition in the Situation Calculus and Probability Theory Frameworks

Demolombe

Fernandez

2006

Lecture Notes in Computer Science

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“…These states can be related to state properties via the formally defined satisfaction relation |=, comparable to the Holds-predicate in the Situation Calculus (see Reiter, 2001 for an introduction, and(Pozos Parra, Nayak, &Demolombe, 2005) for an example application): state(c, t, output) |= p denotes that state property p holds in trace c at time t in the output state of the organism. Based on these statements, dynamic properties can be formulated in a formal manner in a sorted first-order predicate logic with sorts T for time points, Traces for traces and F for state formulae, using quantifiers over time and the usual first-order logical connectives such as ,^, _, ), ", $.…”

Section: Modelling Approachmentioning

confidence: 99%

Representation for reciprocal agent–environment interaction

Bosse¹,

Jonker²,

Treur³

2009

Cognitive Systems Research

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“…O¬position(x, s) represents an ideal fact imposes by the normative system. Here, our interest does not concern the representation of ideal world evolution which must integrate a solution to the corresponding frame problem (see for example [1,5]). Our concern is the design of obedient agents, i.e.…”

Section: Belief Representationmentioning

confidence: 99%

Integrating State Constraints and Obligations in Situation Calculus

Demolombe¹,

Parra

2009

Int. Artif.

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The ramification problem concerns the characterisation of indirect effects of actions. This problem arises when a theory of action is integrated with a set of state constraints. So integrating state constraints to a solution of the frame problem must deal with the ramification problem. In the situation calculus a general solution to both the frame and ramification problems has been proposed. This solution includes the indirect effects of actions in the successor state axioms. On the other hand, in the situation calculus, the notion of belief fluents has been introduced in order to distinguish between facts that hold in a situation and facts that are believed to hold in a situation. So apart from the traditional frame and ramification problems, a belief counterpart of these problems is considered. The successor belief state axioms were proposed to address the belief frame problem. Inspired in the mentioned approaches, we propose a general solution to the belief frame and ramification problems. We consider two sorts of constraints: the believed state constraints relating to physical laws and the believed mental constraints relating to social laws. Constraints imposed by social laws are well know in literature as obligations. Automated reasoning based on the proposal could easily be implemented in Prolog.

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Theories of Intentions in the Framework of Situation Calculus

Cited by 8 publications

References 9 publications

Intention Recognition in the Situation Calculus and Probability Theory Frameworks

Intention Recognition in the Situation Calculus and Probability Theory Frameworks

Representation for reciprocal agent–environment interaction

Integrating State Constraints and Obligations in Situation Calculus

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