Reformulating the Situation Calculus and the Event Calculus in the General Theory of Stable Models and in Answer Set Programming

Lee, Joohyung; Palla, Ravi

doi:10.1613/jair.3489

Cited by 35 publications

(20 citation statements)

References 47 publications

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“…Previous work translated discrete EC into ASP [15,16] by reformulating the EC models as first-order stable models and translating the (almost universal) formulas of EC into a logic program that preserves stable models. Given a finite domain, EC2ASP (and its evolution, F2LP) compile (discrete) Event Calculus formulas into ASP programs [15,16]. This translation scheme relies on two facts: second order circumscription and first order stable model semantics coincide on canonical formulas, and almost-universal formulas can be transformed into a logic program while the stable models are preserved.…”

Section: Related Workmentioning

confidence: 99%

“…Our translation of EC description into s(CASP) program is similar to that of the systems EC2ASP and F2LP [15,16], but we differ in some key aspects that improve performance and are relevant for expressiveness: the treatment of rules with negated heads, the possibility of generating unsafe rules, and the use of constraints over rationals. We describe below, with the help of a running example, the translation that turns logic statements (as found in BEC) into a s(CASP) program.…”

Section: Translating Bec Into S(casp)mentioning

confidence: 99%

“…Answer Set Programming (ASP) is a logic programming paradigm that was initially proposed to realize non-monotonic reasoning [17]. ASP has also been used to model the Event Calculus [15,16]. Classical implementations of ASP are limited to variables ranging over discrete and bound domains and use mechanisms such as grounding and SAT solving to find out models (called answer sets) of ASP programs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modeling and Reasoning in Event Calculus Using Goal-Directed Constraint Answer Set Programming

Arias

Chen

Carro

et al. 2020

Logic-Based Program Synthesis and Transformation

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Automated commonsense reasoning is essential for building human-like AI systems featuring, for example, explainable AI. Event Calculus (EC) is a family of formalisms that model commonsense reasoning with a sound, logical basis. Previous attempts to mechanize reasoning using EC faced difficulties in the treatment of the continuous change in dense domains (e.g., time and other physical quantities), constraints among variables, default negation, and the uniform application of different inference methods, among others. We propose the use of s(CASP), a query-driven, top-down execution model for Predicate Answer Set Programming with Constraints, to model and reason using EC. We show how EC scenarios can be naturally and directly encoded in s(CASP) and how its expressiveness makes it possible to perform deductive and abductive reasoning tasks in domains featuring, for example, constraints involving dense time and fluents.

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Section: Related Workmentioning

confidence: 99%

Section: Translating Bec Into S(casp)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling and Reasoning in Event Calculus Using Goal-Directed Constraint Answer Set Programming

Arias

Chen

Carro

et al. 2020

Logic-Based Program Synthesis and Transformation

View full text Add to dashboard Cite

show abstract

“…The "Event Calculus" was first introduced in a 1986 paper by Bob Kowalski and Marek Sergot [16] as a logic programming framework used to represent and reason about the effects of events or actions [23]. First employed in database applications, it has since then been integrated into other forms of logic programming, classical logic and modal logic, and used in wider contexts such as planning, abductive reasoning or cognitive robotics [18][22] [17].…”

Section: The Event Calculusmentioning

confidence: 99%

Modelling Moral Reasoning and Ethical Responsibility with Logic Programming

Berreby

Bourgne

Ganascia

2015

Lecture Notes in Computer Science

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In this paper, we investigate the use of high-level action languages for representing and reasoning about ethical responsibility in goal specification domains. First, we present a simplified Event Calculus formulated as a logic program under the stable model semantics in order to represent situations within Answer Set Programming. Second, we introduce a model of causality that allows us to use an answer set solver to perform reasoning over the agent's ethical responsibility. We then extend and test this framework against the Trolley Problem and the Doctrine of Double Effect. The overarching aim of the paper is to propose a general and adaptable formal language that may be employed over a variety of ethical scenarios in which the agent's responsibility must be examined and their choices determined. Our fundamental ambition is to displace the burden of moral reasoning from the programmer to the program itself, moving away from current computational ethics that too easily embed moral reasoning within computational engines, thereby feeding atomic answers that fail to truly represent underlying dynamics.

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“…Various intuitionistic fragments of linear logic were hence found to be effective languages in which dynamic state could be natively and effectively represented, avoiding issues such as the frame problem [11,17,17,23,38]. The utility of non-monotonic features were also observed and used by other planners, including those that were based on logic programming and answer sets [9,13,25,26].…”

Section: Introductionmentioning

confidence: 99%

Robotic Task Planning Using a Backchaining Theorem Prover for Multiplicative Exponential First-Order Linear Logic

Kortik

Saranlı

2019

J Intell Robot Syst

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In this paper, we propose an exponential multiplicative fragment of linear logic to encode and solve planning problems efficiently in STRIPS domain, that we call the Linear Planning Logic (LPL). Linear logic is a resource aware logic treating resources as single use assumptions, therefore enabling encoding and reasoning of domains with dynamic state. One of the most important examples of dynamic state domains is robotic task planning, since informational or physical states of a robot include non-monotonic characteristics. Our novel theorem prover is using the backchaining method which is suitable for logic languages like Lolli and Prolog. Additionally, we extend LPL to be able to encode non-atomic conclusions in program formulae. Following the introduction of the language, our theorem prover and its implementation, we present associated algorithmic properties through small but informative examples. Subsequently, we also present a navigation domain using the hexapod robot RHex to show LPL's operation on a real robotic planning problem. Finally, we provide comparisons of LPL with two existing linear logic theorem provers, llprover and linTAP. We show that LPL outperforms these theorem provers for planning domains.

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Reformulating the Situation Calculus and the Event Calculus in the General Theory of Stable Models and in Answer Set Programming

Cited by 35 publications

References 47 publications

Modeling and Reasoning in Event Calculus Using Goal-Directed Constraint Answer Set Programming

Modeling and Reasoning in Event Calculus Using Goal-Directed Constraint Answer Set Programming

Modelling Moral Reasoning and Ethical Responsibility with Logic Programming

Robotic Task Planning Using a Backchaining Theorem Prover for Multiplicative Exponential First-Order Linear Logic

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