The CIFF proof procedure for abductive logic programming with constraints: Theory, implementation and experiments

Mancarella, Paolo; Terreni, Giacomo; Sadri, Fariba; Toni, Francesca; Endriss, Ulle

doi:10.1017/s1471068409990093

Cited by 19 publications

(20 citation statements)

References 48 publications

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“…However, they define compliance as assessing if actions were executed by users with the right permissions (auditing), and focus only on incomplete traces (with complete events), while we take a more sophisticated approach to incompleteness. The adopted abductive framework, CIFF [23], only supports ground abducibles, and ICs are limited to denials. The work in [22] explores also the dimension of human confirmation of hypotheses, and proposes a human-based refinement cycle.…”

Section: Related Workmentioning

confidence: 99%

Abducing Compliance of Incomplete Event Logs

Chesani

Masellis

Francescomarino

et al. 2016

AI*IA 2016 Advances in Artificial Intelligence

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The capability to store data about business processes execution in so-called Event Logs has brought to the diffusion of tools for the analysis of process executions and for the assessment of the goodness of a process model. Nonetheless, these tools are often very rigid in dealing with with Event Logs that include incomplete information about the process execution. Thus, while the ability of handling incomplete event data is one of the challenges mentioned in the process mining manifesto, the evaluation of compliance of an execution trace still requires an endto-end complete trace to be performed. This paper exploits the power of abduction to provide a flexible, yet computationally effective, framework to deal with different forms of incompleteness in an Event Log. Moreover it proposes a refinement of the classical notion of compliance into strong and conditional compliance to take into account incomplete logs. Finally, performances evaluation in an experimental setting shows the feasibility of the presented approach.

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Section: Related Workmentioning

confidence: 99%

Abducing Compliance of Incomplete Event Logs

Chesani

Masellis

Francescomarino

et al. 2016

AI*IA 2016 Advances in Artificial Intelligence

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“…The database is updated by means of an event theory, which uses frame axioms. The ALP agent approach was developed further in the KGP agent model 24,38) . In contrast with both ALP agents and KGP agents, the operational semantics of LPS employs a destructively updated database that represents the current state.…”

Section: Agent Languagesmentioning

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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“…For example CIFF [Endriss et al 2004;Mancarella et al 2009] is a proof procedure for ALPs that also incorporates constraint handling as in constraint logic programming. The constraint handling is used, in particular, for dealing with temporal constraints, for example in planning ].…”

Section: Abductive Logic Programming For Smart Homes For Elderly Carementioning

confidence: 99%

Ambient intelligence

2011

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In this article we survey ambient intelligence (AmI), including its applications, some of the technologies it uses, and its social and ethical implications. The applications include AmI at home, care of the elderly, healthcare, commerce, and business, recommender systems, museums and tourist scenarios, and group decision making. Among technologies, we focus on ambient data management and artificial intelligence; for example planning, learning, event-condition-action rules, temporal reasoning, and agent-oriented technologies. The survey is not intended to be exhaustive, but to convey a broad range of applications, technologies, and technical, social, and ethical challenges.

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The CIFF proof procedure for abductive logic programming with constraints: Theory, implementation and experiments

Cited by 19 publications

References 48 publications

Abducing Compliance of Incomplete Event Logs

Abducing Compliance of Incomplete Event Logs

Reactive Computing as Model Generation

Ambient intelligence

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