A Probabilistic Extension of Action Language

Abstract Action languages are formal models of parts of natural language that are designed to describe effects of actions. Many of these languages can be viewed as high-level notations of answer set programs structured to represent transition systems. However, the form of answer set programs considered in the earlier work is quite limited in comparison with the modern Answer Set Programming (ASP) language, which allows several useful constructs for knowledge representation, such as choice rules, aggregates and abstract constraint atoms. We propose a new action language called BC +, which closes the gap between action languages and the modern ASP language. The main idea is to define the semantics of BC + in terms of general stable model semantics for propositional formulas, under which many modern ASP language constructs can be identified with shorthands for propositional formulas. Language BC + turns out to be sufficiently expressive to encompass the best features of other action languages, such as languages B , C , C + and BC . Computational methods available in ASP solvers are readily applicable to compute BC +, which led to an implementation of the language by extending system cplus2asp .

Download Full-text

Declarative Planning and Knowledge Representation in an Action Language

Intelligent Information Technologies ◽

10.4018/978-1-59904-941-0.ch009 ◽

2011 ◽

pp. 192-221 ◽

Cited By ~ 1

Author(s):

Thomas Eiter ◽

Wolfgang Faber ◽

Gerald Pfeifer

Keyword(s):

Knowledge Representation ◽

Logic Programming ◽

Planning System ◽

Epistemic State ◽

Action Languages ◽

Knowledge States ◽

Action Language ◽

High Level ◽

Planning Problems ◽

Complex Planning

This chapter introduces planning and knowledge representation in the declarative action language K. Rooted in the area of Knowledge Representation & Reasoning, action languages like K allow the formalization of complex planning problems involving non-determinism and incomplete knowledge in a very flexible manner. By giving an overview of existing planning languages and comparing these against our language, we aim on further promoting the applicability and usefulness of high-level action languages in the area of planning. As opposed to previously existing languages for modeling actions and change, K adopts a logic programming view where fluents representing the epistemic state of an agent might be true, false or undefined in each state. We will show that this view of knowledge states can be fruitfully applied to several well-known planning domains from the literature as well as novel planning domains. Remarkably, K often allows to model problems more concisely than previous action languages. All the examples given can be tested in an available implementation, the DLVK planning system.

Download Full-text

Planning with preferences using logic programming

Theory and Practice of Logic Programming ◽

10.1017/s1471068406002717 ◽

2006 ◽

Vol 6 (5) ◽

pp. 559-607 ◽

Cited By ~ 22

Author(s):

TRAN CAO SON ◽

ENRICO PONTELLI

Keyword(s):

Logic Programming ◽

Answer Set Programming ◽

Planning Problem ◽

Declarative Language ◽

High Level ◽

Planning Problems ◽

Answer Set

We present a declarative language, ${\cal PP}$, for the high-level specification of preferences between possible solutions (or trajectories) of a planning problem. This novel language allows users to elegantly express non-trivial, multi-dimensional preferences and priorities over such preferences. The semantics of ${\cal PP}$ allows the identification of most preferred trajectories for a given goal. We also provide an answer set programming implementation of planning problems with ${\cal PP}$ preferences.

Download Full-text

Declarative Planning and Knowledge Representation in an Action Language

Intelligent Techniques for Planning ◽

10.4018/978-1-59140-450-7.ch001 ◽

2005 ◽

pp. 1-34 ◽

Cited By ~ 1

Author(s):

Thomas Eiter ◽

Wolfgang Faber ◽

Gerald Pfeifer ◽

Axel Polleres

Keyword(s):

Knowledge Representation ◽

Logic Programming ◽

Planning System ◽

Epistemic State ◽

Action Languages ◽

Knowledge States ◽

Action Language ◽

High Level ◽

Planning Problems ◽

Complex Planning

This chapter introduces planning and knowledge representation in the declarative action language K. Rooted in the area of Knowledge Representation & Reasoning, action languages like K allow the formalization of complex planning problems involving non-determinism and incomplete knowledge in a very flexible manner. By giving an overview of existing planning languages and comparing these against our language, we aim on further promoting the applicability and usefulness of high-level action languages in the area of planning. As opposed to previously existing languages for modeling actions and change, K adopts a logic programming view where fluents representing the epistemic state of an agent might be true, false or undefined in each state. We will show that this view of knowledge states can be fruitfully applied to several well-known planning domains from the literature as well as novel planning domains. Remarkably, K often allows to model problems more concisely than previous action languages. All the examples given can be tested in an available implementation, the DLVK planning system.

Download Full-text

CoreALMlib: An library translated from the Component Library

Theory and Practice of Logic Programming ◽

10.1017/s1471068416000363 ◽

2016 ◽

Vol 16 (5-6) ◽

pp. 800-816 ◽

Cited By ~ 2

Author(s):

DANIELA INCLEZAN

Keyword(s):

Natural Language ◽

Answer Set Programming ◽

Natural Language Understanding ◽

Inference Engine ◽

Lexical Resources ◽

Language Understanding ◽

Commonsense Knowledge ◽

Component Library ◽

Action Language ◽

Answer Set

AbstractThis paper presents CoreALMlib, an $\mathscr{ALM}$ library of commonsense knowledge about dynamic domains. The library was obtained by translating part of the Component Library (CLib) into the modular action language $\mathscr{ALM}$. CLib consists of general reusable and composable commonsense concepts, selected based on a thorough study of ontological and lexical resources. Our translation targets CLibstates (i.e., fluents) and actions. The resulting $\mathscr{ALM}$ library contains the descriptions of 123 action classes grouped into 43 reusable modules that are organized into a hierarchy. It is made available online and of interest to researchers in the action language, answer-set programming, and natural language understanding communities. We believe that our translation has two main advantages over its CLib counterpart: (i) it specifies axioms about actions in a more elaboration tolerant and readable way, and (ii) it can be seamlessly integrated with ASP reasoning algorithms (e.g., for planning and postdiction). In contrast, axioms are described in CLib using STRIPS-like operators, and CLib's inference engine cannot handle planning nor postdiction.

Download Full-text

Modular action language

Theory and Practice of Logic Programming ◽

10.1017/s1471068415000095 ◽

2015 ◽

Vol 16 (2) ◽

pp. 189-235 ◽

Cited By ~ 3

Author(s):

DANIELA INCLEZAN ◽

MICHAEL GELFOND

Keyword(s):

Logic Programming ◽

Medium Size ◽

Programming System ◽

Problem Structuring ◽

System Description ◽

Action Languages ◽

Special Cases ◽

Action Language ◽

Stepwise Development ◽

High Level

AbstractThe paper introduces a new modular action language,${\mathcal ALM}$, and illustrates the methodology of its use. It is based on the approach of Gelfond and Lifschitz (1993,Journal of Logic Programming 17, 2–4, 301–321; 1998,Electronic Transactions on AI 3, 16, 193–210) in which a high-level action language is used as a front end for a logic programming system description. The resulting logic programming representation is used to perform various computational tasks. The methodology based on existing action languages works well for small and even medium size systems, but is not meant to deal with larger systems that requirestructuring of knowledge.$\mathcal{ALM}$is meant to remedy this problem. Structuring of knowledge in${\mathcal ALM}$is supported by the concepts ofmodule(a formal description of a specific piece of knowledge packaged as a unit),module hierarchy, andlibrary, and by the division of a system description of${\mathcal ALM}$into two parts:theoryandstructure. Atheoryconsists of one or more modules with a common theme, possibly organized into a module hierarchy based on adependency relation. It contains declarations of sorts, attributes, and properties of the domain together with axioms describing them.Structuresare used to describe the domain's objects. These features, together with the means for defining classes of a domain as special cases of previously defined ones, facilitate the stepwise development, testing, and readability of a knowledge base, as well as the creation of knowledge representation libraries.

Download Full-text

Chapter 34. Stochastic Boolean Satisfiability

Frontiers in Artificial Intelligence and Applications - Handbook of Satisfiability ◽

10.3233/faia201018 ◽

2021 ◽

Author(s):

Stephen M. Majercik

Keyword(s):

Artificial Intelligence ◽

Computational Complexity ◽

Probabilistic Reasoning ◽

Broad Class ◽

Theoretical Perspective ◽

Boolean Satisfiability ◽

Probabilistic Planning ◽

Empirical Results ◽

Future Work ◽

Planning Problems

Stochastic satisfiability (SSAT) is an extension of satisfiability (SAT) that merges two important areas of artificial intelligence: logic and probabilistic reasoning. Initially suggested by Papadimitriou, who called it a “game against nature”, SSAT is interesting both from a theoretical perspective–it is complete for PSPACE, an important complexity class–and from a practical perspective–a broad class of probabilistic planning problems can be encoded and solved as SSAT instances. This chapter describes SSAT and its variants, their computational complexity, applications of SSAT, analytical results, algorithms and empirical results, related work, and directions for future work.

Download Full-text

Action-Centered Information Retrieval

Theory and Practice of Logic Programming ◽

10.1017/s1471068419000097 ◽

2019 ◽

Vol 20 (2) ◽

pp. 249-272

Author(s):

MARCELLO BALDUCCINI ◽

EMILY C. LEBLANC

Keyword(s):

Information Retrieval ◽

Side Effects ◽

Answer Set Programming ◽

The State ◽

Semantic Level ◽

Action Language ◽

The World ◽

Answer Set

AbstractInformation retrieval (IR) aims at retrieving documents that are most relevant to a query provided by a user. Traditional techniques rely mostly on syntactic methods. In some cases, however, links at a deeper semantic level must be considered. In this paper, we explore a type of IR task in which documents describe sequences of events, and queries are about the state of the world after such events. In this context, successfully matching documents and query requires considering the events’ possibly implicit uncertain effects and side effects. We begin by analyzing the problem, then propose an action language-based formalization, and finally automate the corresponding IR task using answer set programming.

Download Full-text

Representing hybrid automata by action language modulo theories

Theory and Practice of Logic Programming ◽

10.1017/s1471068417000412 ◽

2017 ◽

Vol 17 (5-6) ◽

pp. 924-941 ◽

Cited By ~ 1

Author(s):

JOOHYUNG LEE ◽

NIKHIL LONEY ◽

YUNSONG MENG

Keyword(s):

Differential Equations ◽

Ordinary Differential Equations ◽

Satisfiability Modulo Theories ◽

Prototype System ◽

Hybrid Automata ◽

Action Languages ◽

Smt Solver ◽

Action Language ◽

Linear Hybrid Automata ◽

Answer Set

AbstractBoth hybrid automata and action languages are formalisms for describing the evolution of dynamic systems. This paper establishes a formal relationship between them. We show how to succinctly represent hybrid automata in an action language which in turn is defined as a high-level notation for answer set programming modulo theories—an extension of answer set programs to the first-order level similar to the way satisfiability modulo theories (SMT) extends propositional satisfiability (SAT). We first show how to represent linear hybrid automata with convex invariants by an action language modulo theories. A further translation into SMT allows for computing them using SMT solvers that support arithmetic over reals. Next, we extend the representation to the general class of non-linear hybrid automata allowing even non-convex invariants. We represent them by an action language modulo ordinary differential equations, which can be compiled into satisfiability modulo ordinary differential equations. We present a prototype systemcplus2aspmtbased on these translations, which allows for a succinct representation of hybrid transition systems that can be computed effectively by the state-of-the-art SMT solverdReal.

Download Full-text

Online Learning Probabilistic Event Calculus Theories in Answer Set Programming

Theory and Practice of Logic Programming ◽

10.1017/s1471068421000107 ◽

2021 ◽

pp. 1-25

Author(s):

NIKOS KATZOURIS ◽

GEORGIOS PALIOURAS ◽

ALEXANDER ARTIKIS

Keyword(s):

Probabilistic Reasoning ◽

Answer Set Programming ◽

Predictive Performance ◽

Theory And Practice ◽

Markov Logic ◽

Event Calculus ◽

Maritime Surveillance ◽

Novel Approach ◽

Combine Uncertainty ◽

Answer Set

Abstract Complex Event Recognition (CER) systems detect event occurrences in streaming time-stamped input using predefined event patterns. Logic-based approaches are of special interest in CER, since, via Statistical Relational AI, they combine uncertainty-resilient reasoning with time and change, with machine learning, thus alleviating the cost of manual event pattern authoring. We present a system based on Answer Set Programming (ASP), capable of probabilistic reasoning with complex event patterns in the form of weighted rules in the Event Calculus, whose structure and weights are learnt online. We compare our ASP-based implementation with a Markov Logic-based one and with a number of state-of-the-art batch learning algorithms on CER data sets for activity recognition, maritime surveillance and fleet management. Our results demonstrate the superiority of our novel approach, both in terms of efficiency and predictive performance. This paper is under consideration for publication in Theory and Practice of Logic Programming (TPLP).

Download Full-text