Chapter 19 Nonmonotonic Causal Logic

Turner, Hudson

doi:10.1016/s1574-6526(07)03019-2

Cited by 3 publications

(2 citation statements)

References 31 publications

(36 reference statements)

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“…HCP lan utilizes the causality-based non-monotonic logic formalism of the action description language C + (Giunchiglia et al, 2004) to represent hybrid deterministic actuation actions and hybrid non-deterministic sensing actions, and the automated reasoning system CCalc (McCain and Turner, 1997) to compute branches of hybrid conditional plans. Let us briefly describe the language C + , while we refer the readers to the articles that provide more details about this formalism (Giunchiglia et al, 2004; Turner, 2008) and the system (McCain and Turner, 1997).…”

Section: Action Description Language C +mentioning

confidence: 99%

“…Therefore, solving a computation task (i.e., the computation of each branch of a hybrid conditional plan) also requires cognitive abilities beyond hybrid classical planning. For that, HCP lan adapts some advantages of causality-based non-monotonic logics (Gelfond and Lifschitz, 1998; Turner, 2008) for a novel solution to deal with these challenges. In particular, HCP lan utilizes defaults to express assumptions (e.g., the location of an object remains to be the same unless changed by an actuation action), exogeneity of actions to express that sensing actions may occur at any time when possible, and non-deterministic causal laws to choose an outcome of a sensing action included in the computation of a branch.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid conditional planning for robotic applications

Nouman

Patoğlu

Erdem

2020

The International Journal of Robotics Research

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Robots who have partial observability of and incomplete knowledge about their environments may have to consider contingencies while planning, and thus necessitate cognitive abilities beyond classical planning. Moreover, during planning, they need to consider continuous feasibility checks for executability of the plans in the real world. Conditional planning is concerned with reaching goals from an initial state, in the presence of incomplete knowledge and partial observability, by considering all contingencies and by utilizing sensing actions to gather relevant knowledge when needed. A conditional plan is essentially a tree of actions where each branch of the tree represents a possible execution of actuation actions and sensing actions to reach a goal state. Hybrid conditional planning extends conditional planning by integrating feasibility checks into executability conditions of actions. We introduce a parallel offline algorithm, called HCPlan, for computing hybrid conditional plans. HCPlan relies on modeling deterministic effects of actuation actions and non-deterministic effects of sensing actions in the causality-based action language [Formula: see text]. Branches of a hybrid conditional plan are computed in parallel using a SAT solver, where continuous feasibility checks are performed as needed. We develop a comprehensive benchmark suite and introduce new evaluation metrics for hybrid conditional planning. We evaluate HCPlan with extensive experiments in terms of computational efficiency and plan quality. We perform experiments to compare HCPlan with other related conditional planners and approaches to deal with contingencies due to incomplete knowledge. We further demonstrate the applicability and usefulness of HCPlan in service robotics applications, through dynamic simulations and physical implementations.

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Section: Action Description Language C +mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid conditional planning for robotic applications

Nouman

Patoğlu

Erdem

2020

The International Journal of Robotics Research

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Knowledge Representation for Philosophers

Thomason

2018

Introduction to Formal Philosophy

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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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Chapter 19 Nonmonotonic Causal Logic

Cited by 3 publications

References 31 publications

Hybrid conditional planning for robotic applications

Hybrid conditional planning for robotic applications

Knowledge Representation for Philosophers

Reactive Computing as Model Generation

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