FLUX: A logic programming method for reasoning agents

Thielscher, Michael

doi:10.1017/s1471068405002358

Cited by 102 publications

(74 citation statements)

References 22 publications

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“…FLUX 50) is a constraint logic programming language for the design of intelligent agents that reason about their actions using the fluent calculus, in which states are represented as lists. Although it is claimed that updates are performed destructively, the list representation of states requires the explicit use of recursion both to query whether a fluent is a member of a state, and to delete the fluent if it is terminated by an action.…”

Section: Event E: Conditions C  Goals G and Actions Amentioning

confidence: 99%

Reactive Computing as Model Generation

Kowalski

Sadri

2015

New Gener. Comput.

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In this paper we propose a logic-based, framework inspired by artificial intelligence, but scaled down for practical database and programming applications. Computation in the framework is viewed as the task of generating a sequence of state transitions, with the purpose of making an agent's goals all true. States are represented by sets of atomic sentences (or facts), representing the values of program variables, tuples in a coordination language, facts in relational databases, or Herbrand models.In the model-theoretic semantics, the entire sequence of states and events are combined into a single model-theoretic structure, by associating timestamps with facts and events. But in the operational semantics, facts are updated destructively, without timestamps. We show that the model generated by destructive updates is identical to the model generated by reasoning with facts containing timestamps. We also extend the model with intentional predicates and composite event predicates defined by logic programs containing conditions in first-order logic, which query the current state. §1 IntroductionIn this paper, we present a computational framework that uses logic for state transition systems. Although the approach has its origins in research about representing and reasoning about states, actions and events in artificial intelligence, it has been scaled-down to make it suitable for more conventional computer applications. It builds on logic programming, but includes imperative language features, including reactive rules and destructive change of state.In earlier versions 29,30,31,32) of this work, we referred to the framework as LPS, to highlight its focus on providing a Logic-based approach to Production Systems. In this paper, for the sake of continuity, we retain the name LPS, although the intended applications of the approach have been extended considerably. These applications include its use for agent programming, active databases, concurrent systems, and composite (or complex) event processing.The paper is organised as follows: Section 2 presents an overview of the framework, and Section 3 illustrates the framework by means of examples. Section 4 defines the language more formally, and Section 5 presents the operational semantics and discusses our implementations. Section 6 discusses soundness and completeness. In particular, it shows that destructive updates in LPS generate the same models as the frame axiom. Sections 7 and 8 discuss related and future work.Compared with earlier papers, the main contributions of this paper are its more rigorous treatment of the semantics of reactive rules and logic programs

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Section: Event E: Conditions C  Goals G and Actions Amentioning

confidence: 99%

Reactive Computing as Model Generation

Kowalski

Sadri

2015

New Gener. Comput.

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“…With FLUX (Thielscher 2002a;Thielscher 2002b;Thielscher 2005), which stands for FLUent eXecutor, Thielscher introduces a kind of run-time system for the fluent calculus. Constrained logic programs encode agents' tasks based on the so-called FLUX kernel which implements the state update axioms.…”

Section: Fluent Calculusmentioning

confidence: 99%

“…The states reachable with the program form the state space of the option. Note that we use the term state here and use it similar as is done in related action formalisms like FLUX (Thielscher 2005) (cf. also Chapter 2.1.5, page 18).…”

Section: Generating a Dt Plan Librarymentioning

confidence: 99%

Robot Controllers for Highly Dynamic Environments with Real-time Constraints

Ferrein

2010

Künstl Intell

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“…[12] describes how event calculus [13,14] can be used to provide high-level control for a Khepera robot. The agent programming language FLUX [15], based on the fluent calculus [16], has also been used to control the execution of some robots. 3 For instance, [17] presents how FLUX can be used for monitoring the execution of a plan, on a Pioneer 2 mobile robot.…”

Section: Introductionmentioning

confidence: 99%

Bridging the Gap between High-Level Reasoning and Low-Level Control

Çaldıran

Haspalamutgil

et al. 2009

Logic Programming and Nonmonotonic Reasoning

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Abstract. We present a formal framework where a nonmonotonic formalism (the action description language C+) is used to provide robots with high-level reasoning, such as planning, in the style of cognitive robotics. In particular, we introduce a novel method that bridges the high-level discrete action planning and the lowlevel continuous behavior by trajectory planning. We show the applicability of this framework on two LEGO MINDSTORMS NXT robots, in an action domain that involves concurrent execution of actions that cannot be serialized.

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FLUX: A logic programming method for reasoning agents

Cited by 102 publications

References 22 publications

Reactive Computing as Model Generation

Reactive Computing as Model Generation

Robot Controllers for Highly Dynamic Environments with Real-time Constraints

Bridging the Gap between High-Level Reasoning and Low-Level Control

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