Preference representation and reasoning

With the increasing speed and capacity of answer set solvers and showcase applications in a variety of fields, Answer Set Programming (ASP) is maturing as a programming paradigm for declarative problem solving. Comprehensive programming methodologies have been developed for procedural and object-oriented paradigms to assist programmers in developing their programs from the problem specification. In many cases, however it is not clear how, or even if, such methodologies can be applied to answer set programming. In this paper, we present a first and rather pragmatic methodology for ASP and illustrate our approach through the encoding of graphical puzzle. Work supported by the ALIVE project (FP7 215890) 1 http://wasp.unime.it/

In formal systems for reasoning about actions, the ramification problem denotes the problem of handling indirect effects. These effects are not explicitly represented in action specifications but follow from general laws describing dependencies among components of the world state. An adequate treatment of indirect effects requires a suitably weakened version of the general law of persistence. It also requires a method to avoid unintuitive changes suggested by the aforementioned dependency laws. We propose a solution to the ramification problem that uses directed relations between two single effects, stating the circumstances under which the occurrence of the first causes the second. We argue for the necessity of an approach based on causality by elaborating the limitations of common paradigms employed to handle ramifications-the principle of categorization and the policy of minimal change. Our abstract solution is realized on the basis of a particular action calculus, namely, the fluent calculus.

Logic programs with ordered disjunction have shown to be a flexible specification language able to model common user preferences in a natural way. However, in some realistic scenarios the preferences should be linked to the evidence of the information when trying to reach a single preferred solution. In this paper, we extend the syntax and the semantics of logic programs with ordered disjunction in order to cope with uncertain information. In particular, we define a possibilistic semantics for capturing possibilistic ordered disjunction programs. We use a simple example to explain the approach and outline an application scenario showing the benefits of possibilistic ordered disjunction.

Very often, we have to look into multiple agents' preferences, and compare or aggregate them. In this paper, we consider the well-known model, namely, lexicographic preference trees (LP-trees), for representing agents' preferences in combinatorial domains. We tackle the problem of calculating the dissimilarity/distance between agents' LPtrees. We propose an algorithm LpDis to compute the number of disagreed pairwise preferences between agents by traversing their LP-trees. The proposed algorithm is computationally efficient and allows agents to have different attribute importance structures and preference dependencies.

Very often, we have to look into multiple agents' preferences, and compare or aggregate them. In this paper, we consider the well-known model, namely, lexicographic preference trees (LP-trees), for representing agents' preferences in combinatorial domains. We tackle the problem of calculating the dissimilarity/distance between agents' LP-trees. We propose an algorithm LpDis to compute the number of disagreed pairwise preferences between agents by traversing their LP-trees. The proposed algorithm is computationally efficient and allows agents to have different attribute importance structures and preference dependencies. 

Answer Set Programming (ASP) is a well-established declarative problem solving paradigm which became widely used in AI and recognized as a powerful tool for knowledge representation and reasoning (KRR), especially for its high expressiveness and the ability to deal also with incomplete knowledge. Recently, thanks to the availability of a number of robust and efficient implementations, ASP has been increasingly employed in a number of different domains, and used for the development of industrial-level and enterprise applications. This made clear the need for proper development tools and interoperability mechanisms for easing interaction and integration with external systems in the widest range of real-world scenarios, including mobile applications and educational contexts. In this work we present a framework for integrating the KRR capabilities of ASP into generic applications. We show the use of the framework by illustrating proper specializations for some relevant ASP systems over different platforms, including the mobile setting; furthermore, the potential of the framework for educational purposes is illustrated by means of the development of several ASP-based applications.

Designing and implementing AI in games is an interesting, yet complex task. This paper briefly presents some applications that make use of Answer Set Programming for such a task, and show some advantages of declarative programming frameworks against imperative (algorithmic) approaches while dealing with knowledge representation and reasoning: solid theoretical bases, no need for algorithm design or coding, explicit (and thus easily modifiable/upgradeable) knowledge representation, declarative specifications which are already executable, very fast prototyping, quick error detection, modularity.

ion is a powerful technique that has not been considered much for nonmonotonic reasoning formalisms including Answer Set Programming (ASP), apart from related simplification methods. We introduce a notion for abstracting from the domain of an ASP program that shrinks the domain size and overapproximates the set of answer sets, as well as an abstraction-&-refinement methodology that, starting from an initial abstraction, automatically yields an abstraction with an associated answer set matching an answer set of the original program if one exists. Experiments reveal the potential of the approach, by its ability to focus on the program parts that cause unsatisfiability and by achieving concrete abstract answer sets that merely reflect relevant details.

Declarative programming allows the expression of properties of the desired solution(s), while the computational task is delegated to a general-purpose algorithm. The freedom from explicit control is counterbalanced by the difficulty in working out what properties are missing or are incorrectly expressed, when the solutions do not meet expectations. This can be particularly problematic in the case of answer set semantics, because the absence of a key constraint/rule could make the difference between none or thousands of answer sets, rather than the intended one (or handful). The debugging task then comprises adding or deleting conditions on the right hand sides of existing rules or, more far-reaching, adding or deleting whole rules. The contribution of this paper is to show how inductive logic programming (ILP) along with examples of (un)desirable properties of answer sets can be used to revise the original program semi-automatically so that it satisfies the stated properties, in effect providing debugging-by-example for programs under answer set semantics.

This paper presents COREALMLIB, an 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 ALM. CLIB consists of general reusable and composable commonsense concepts, selected based on a thorough study of ontological and lexical resources. Our translation targets CLIB states (i.e., fluents) and actions. The resulting 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. Under consideration for publication in TPLP.

This paper presents a library of commonsense knowledge, RESTKB, developed in modular action language ALM and containing background knowledge relevant to the understanding of restaurant narratives, including stories that describe exceptions to the normal unfolding of such scenarios. We highlight features that KR languages must possess in order to be able to express pertinent knowledge, and expand action language ALM as needed. We show that encoding the knowledge base in ALM facilitates its piecewise construction and testing, and improves the generality and quality of the captured information, in comparison to an initial ASP encoding. The knowledge base was used in a system for reasoning about stereotypical activities, evaluated on the restaurant domain.

This paper presents COREALMLIB, an 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 ALM. CLIB consists of general reusable and composable commonsense concepts, selected based on a thorough study of ontological and lexical resources. Our translation targets CLIB states (i.e., fluents) and actions. The resulting 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. Under consideration for publication in TPLP.

The paper introduces a new modular action language, ALM, and illustrates the methodology of its use. It is based on the approach of Gelfond and Lifschitz (1993; 1998) 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 require structuring of knowledge. ALM is meant to remedy this problem. Structuring of knowledge in ALM is supported by the concepts of module (a formal description of a specific piece of knowledge packaged as a unit), module hierarchy, and library, and by the division of a system description of ALM into two parts: theory and structure. A theory consists of one or more modules with a common theme, possibly organized into a module hierarchy based on a dependency relation. It contains declarations of sorts, attributes, and properties of the domain together with axioms describing them. Structures are 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.

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 to formalize 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 DLV K planning system.

With the increasing efficiency of answer set solvers and a better understanding of program design, answer set programming has reached a stage where it can be more successfully applied in a wider range of applications and where it attracts attention from researchers in other disciplines. One of these domains is music synthesis.< br/> In this paper we approach the automation and analysis of composition of music as a knowledge representation and advanced reasoning task. Doing so, it is possible to capture the underlying rules of ...

With the increasing speed and capacity of answer set solvers and showcase applications in a variety of fields, Answer Set Programming (ASP) is maturing as a programming paradigm for declarative problem solving. Com-prehensive programming methodologies have been developed for procedural and object-oriented paradigms to assist programmers in developing their programs from the problem specification. In many cases, however it is not clear how, or even if, such methodologies can be applied to answer set programming. In ...

We present an overview on how to perform non-monotonic reasoning based on paraconsistent logics. In particular, we show that one can define a logic programming semantics based on the paraconsistent logic G' 3 which is called G' 3-stable semantics. This semantics defines a frame for performing non-monotonic reasoning in domains which are pervaded with vagueness and inconsistencies. In fact, we show that, by considering also a possibilistic logic point of view, one can use this extended framework for defining a possibilistic logic programming approach able to deal with reasoning, which is at the same time non-monotonic and uncertain.

In this paper we present a language to reason about actions in a probabilistic setting and compare our work with earlier work by Pearl.The main feature of our language is its use of static and dynamic causal laws, and use of unknown (or background) variables - whose values are determined by factors beyond our model - in incorporating probabilities. We use two kind of unknown variables: inertial and non-inertial. Inertial unknown variables are helpful in assimilating observations and modeling counterfactuals and causality; while non-inertial unknown variables help characterize stochastic behavior, such as the outcome of tossing a coin, that are not impacted by observations. Finally, we give a glimpse of incorporating probabilities into reasoning with narratives.

Pearl's probabilistic causal model has been used in many domains to reason about causality. Pearl's treatment of actions is very different from the way actions are represented (explicitly) and their impact is reasoned in most other papers in the literature. In this paper we show how to encode Pearl's probabilistic causal model in the action language PAL thus relating this two distinct approaches to reason about actions.

In this paper we present a language to reason about actions in a probabilistic setting and compare our work with earlier work by Pearl.The main feature of our language is its use of static and dynamic causal laws, and use of unknown (or background) variables - whose values are determined by factors beyond our model - in incorporating probabilities. We use two kind of unknown variables: inertial and non-inertial. Inertial unknown variables are helpful in assimilating observations and modeling counterfactuals and causality; while non-inertial unknown variables help characterize stochastic behavior, such as the outcome of tossing a coin, that are not impacted by observations. Finally, we give a glimpse of incorporating probabilities into reasoning with narratives.

Pearl's probabilistic causal model has been used in many domains to reason about causality. Pearl's treatment of actions is very different from the way actions are represented (explicitly) and their impact is reasoned in most other papers in the literature. In this paper we show how to encode Pearl's probabilistic causal model in the action language PAL thus relating this two distinct approaches to reason about actions.

ion is a powerful technique that has not been considered much for nonmonotonic reasoning formalisms including Answer Set Programming (ASP), apart from related simplification methods. We introduce a notion for abstracting from the domain of an ASP program that shrinks the domain size and overapproximates the set of answer sets, as well as an abstraction-&-refinement methodology that, starting from an initial abstraction, automatically yields an abstraction with an associated answer set matching an answer set of the original program if one exists. Experiments reveal the potential of the approach, by its ability to focus on the program parts that cause unsatisfiability and by achieving concrete abstract answer sets that merely reflect relevant details.

Reasoning about preferences is a major issue in many decision making problems. Recently, a new logic for handling preferences, called qualitative choice logic (QCL), was presented. This logic adds to classical propositional logic a new connective, called ordered disjunction symbolized by ×. That new connective is used to express preferences between alternatives. Intuitively, if A and B are propositional formulas then A ×B means: "if possible A, but if A is impossible then at least B". One of the important limitations of QCL is that it does not correctly deal with negated and conditional preferences. Conditional rules that involve preferences are expressed using propositional implication. However, using QCL semantics, there is no difference between such material implication "(KLM ×Air France) ⇒ Hotel Package" and the purely propositional formula "(Air France∨KLM) ⇒ Hotel Package". Moreover, the negation in QCL misses some desirable properties from propositional calculus. This paper first proposes an extension of QCL language to universally quantified first-order logic framework. Then, we propose two new logics that correctly address QCL's limitations. Both of them are based on the same QCL language, but define new non-monotonic consequence relations. The first logic, called PQCL (prioritized qualitative choice logic), is particularly adapted for handling prioritized preferences, while the second one, called QCL+ (positive qualitative choice logic), is appropriate for handling positive preferences. In both cases, we show that any set of preferences, can equivalently be transformed into a set of basic preferences from which efficient inferences can be applied. Lastly, we show how our logics can be applied to alert correlation.

Reasoning about preferences is a major issue in many decision making problems. Recently, a new logic for handling preferences, called Qualitative Choice Logic (QCL), was presented. This logic adds to classical propositional logic a new connective, called ordered disjunction symbolized by ×. That new connective is used to express preferences between alternatives. QCL was not designed to handle conditional preferences, even if it is possible to express an implication with a preference on the left hand side, for instance "(Air France × Virgin) ⇒ Hotel Package". However, using QCL semantics, there is no difference between such material implication "(Virgin × Air France) ⇒ Hotel Package" and the purely propositional formula "(Air France ∨ Virgin) ⇒ Hotel Package ". Indeed, the negation in QCL gets rid of nested ordered disjunctions. Furthermore, the negation in QCL misses some desirable properties from propositional calculus. In this paper, we present a new semantics for the QCL language that addresses those problems. We describe a general framework for handling qualitative preferences. That framework is based on normal form functions that transform general QCL formulas into basic choice formulas, which are simple formulas (ordered disjunction of propositional formulas). We formulate the properties of our normal form function that overcome current QCL limitations.

We present a logic programming based conditional planner that is capable of generating both conditional plans and conformant plans in the presence of sensing actions and incomplete information. We prove the correctness of our implementation and show that our planner is complete with respect to the 0approximation of sensing actions and the class of conditional plans considered in this paper. Finally, we present preliminary experimental results and discuss further enhancements to the program.

In this paper we extend the logic programming based conformant planner described in [Son et al., 2005a] to allow it to work on planning problems with more complex descriptions of the initial states. We also compare the extended planner with other ...

This paper describes our methodology for building conformant planners, which is based on recent advances in the theory of action and change and answer set programming. The development of a planner for a given dynamic domain starts with encoding the knowledge about fluents and actions of the domain as an action theory D of some action language. Our choice in this paper is AL-an action language with dynamic and static causal laws and executability conditions. An action theory D of AL defines a transition diagram T (D) containing all the possible trajectories of the domain. A transition s, a, s belongs to T (D) iff the execution of the action a in the state s may move the domain to the state s. The second step in the planner development consists in finding a deterministic transition diagram T lp (D) such that nodes of T lp (D) are partial states of D, its arcs are labeled by actions, and a path in T lp (D) from an initial partial state δ 0 to a partial state satisfying the goal δ f corresponds to a conformant plan for δ 0 and δ f in T (D). The transition diagram T lp (D) is called an 'approximation' of T (D). We claim that a concise description of an approximation of T (D) can often be given by a logic program π(D) under the answer sets semantics. Moreover, complex initial situations and constraints on plans can be also expressed by logic programming rules and included in π(D). If this is possible then the problem of finding a parallel or sequential conformant plan can be reduced to computing answer sets of π(D). This can be done by general purpose answer set solvers. If plans are sequential and long then this method can be too time consuming. In this case, π(D) is used as a specification for a procedural graph searching conformant planning algorithm. The paper illustrates this methodology by building several conformant planners which work for domains with complex relationship between the fluents. The efficiency of the planners is experimentally Preprint submitted to Elsevier Science evaluated on a number of new and old benchmarks. In addition we show that for a subclass of action theories of AL our planners are complete, i.e., if in T lp (D) we cannot get from δ 0 to a state satisfying the goal δ f then there is no conformant plan for δ 0 and δ f in T (D).

I would like to thank my advisor Vladimir Lifschitz for making this dissertation possible. This project grew from the seed of an idea he recommended initially, and my work thereafter has been guided by his many incisive observations and helpful suggestions. His very precise analysis of all problems he encounters and his formal approach always lead to great insight into the research questions we encounter. These are skills that I hope to have at least partially acquired during my time working with him and I feel grateful to have been lucky enough to be his student. I also wish to thank the other members of my dissertation committee, for serving on my committee and for their useful comments on my dissertation. Michael Gelfond is a master of attention to detail and helped me clarify several aspects of this work. Ben Kuipers encouraged me to think about the relation of my research to researchers in other areas. Bruce Porter shared his experience in building large knowledge bases and helped me keep the long-term goals of such projects in mind. Peter Stone's comments helped me think about how the success of such work may be measured. I would also like to acknowledge his friendly guidance and leadership during the time I spent working with the Austin Villa robot soccer team at U.T. I have had the privilege to be in a research group with many wonderful graduate students and I extend my thanks to Esra Erdem, Paolo Ferraris, Joohyung Lee, Yuliya Lierler, and Wanwan Ren for all of our good times during the interesting conversations in the office, the trips to conferences, and the parties. v Studying at a great school like U.T. gave me the opportunity to meet many smart and nice people. Thanks to all my friends in the Computer Sciences department and also those from the Turkish University Students Association. They are too numerous to mention. However, I would especially like to thankÖzgür Erciyes, for his exceptional friendship, support and encouragement. The National Science Foundation provided partial financial support for my research. The greatest support for everything I have done in my life has come from my parents, Turhan and Phyllis Erdogan, and my brother, Sinan Erdogan. My parents were always by my side, even with the many miles between us, constantly encouraging me, keeping my spirits up, and sharing their experience. Sinan was not only my brother, but my roommate, best friend, and most ardent supporter in Austin. Thanks to them from the bottom of my heart.

Most belief change operators in the AGM tradition assume an underlying plausibility ordering over the possible worlds which is transitive and complete. A unifying structure for these operators, based on supplementing the plausibility ordering with a second, guiding, relation over the worlds was presented in [7]. However it is not always reasonable to assume completeness of the underlying ordering. In this paper we generalise the structure of [7] to allow incomparabilities between worlds. We axiomatise the resulting class of belief removal functions, and show that it includes an important family of removal functions based on finite prioritised belief bases.

In this paper, we propose a new constructive characterization of those semantics for disjunctive logic programs which are extensions of the well-founded semantics for normal programs. Based on considerations about how disjunctive information is treated by a given semantics, we divide the computation of that semantics into two phases. The first one is a program transformation phase, which applies axiom schemata expressing how derivations involving disjunctions are made in the given semantic framework. The second one is a constructive phase, based on a variation of the well-founded model construction for normal programs. We apply this two-phases procedural semantics to the computation of the static semantics of disjunctive logic programs as a case-study, showing how it works and what its results are in several examples. A main perspective of this proposal is a procedural semantics for disjunctive programs consisting of an inefficient preprocessing phase (implementing the program transformation procedure), to be however performed only once, and of an efficient runtime computation, obtained as a variation of any effective procedural semantics for the well-founded model. We thank the anonymous referees for their careful readings of the manuscript and many useful comments.

Over the last ten years, answer set programming (ASP) has grown from a pure theoretical knowledge representation and reasoning formalism to a computational approach with a very strong formal backing. At present, ASP is seen as the computational embodiment of non-monotonic reasoning incorporating techniques of databases, knowledge representation, logic and constraint programming. ASP has become an appealing tool for knowledge representation and reasoning and thanks to the increasing efficiency of the ...

Answer Set Programming (ASP) is a well-established declarative problem solving paradigm which became widely used in AI and recognized as a powerful tool for knowledge representation and reasoning (KRR), especially for its high expressiveness and the ability to deal also with incomplete knowledge. Recently, thanks to the availability of a number of robust and efficient implementations, ASP has been increasingly employed in a number of different domains, and used for the development of industrial-level and enterprise applications. This made clear the need for proper development tools and interoperability mechanisms for easing interaction and integration with external systems in the widest range of real-world scenarios, including mobile applications and educational contexts. In this work we present a framework for integrating the KRR capabilities of ASP into generic applications. We show the use of the framework by illustrating proper specializations for some relevant ASP systems over different platforms, including the mobile setting; furthermore, the potential of the framework for educational purposes is illustrated by means of the development of several ASP-based applications.

Answer Set Programming (ASP) is a fully-declarative logic programming paradigm, which has been proposed in the area of knowledge representation and non-monotonic reasoning. Nowadays, the formal properties of ASP are well-understood, efficient ASP systems are available, and, recently, ASP has been employed in a few industrial applications. However, ASP technology is not mature for a successful exploitation in industry yet; mainly because ASP technologies are not integrated in the well-assessed ...

This paper presents COREALMLIB, an 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 ALM. CLIB consists of general reusable and composable commonsense concepts, selected based on a thorough study of ontological and lexical resources. Our translation targets CLIB states (i.e., fluents) and actions. The resulting 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. Under consideration for publication in TPLP.

This paper describes a modular action language, ALM, dedicated to the specification of complex dynamic systems. One of the main goals of the language is to facilitate the development and testing of knowledge representation libraries. We present the implementation of a large scale library of commonsense concepts, achieved by porting knowledge from the Component Library (CLIB) into ALM. Our choice of CLIB as a source of inspiration is justified by the well-founded methodology used by its authors in selecting the general concepts it contains, and its extensive testing in the context of the Automated User-centered Reasoning and Acquisition System. The resulting ALM library has the additional advantage of incorporating established knowledge representation methodologies developed in the action language research community.

This paper presents a library of commonsense knowledge, RESTKB, developed in modular action language ALM and containing background knowledge relevant to the understanding of restaurant narratives, including stories that describe exceptions to the normal unfolding of such scenarios. We highlight features that KR languages must possess in order to be able to express pertinent knowledge, and expand action language ALM as needed. We show that encoding the knowledge base in ALM facilitates its piecewise construction and testing, and improves the generality and quality of the captured information, in comparison to an initial ASP encoding. The knowledge base was used in a system for reasoning about stereotypical activities, evaluated on the restaurant domain.

This paper presentsCoreALMlib, an$\mathscr{ALM}$library of commonsense knowledge about dynamic domains. The library was obtained by translating part of theComponent Library(CLib) into the modular action language$\mathscr{ALM}$.CLibconsists of general reusable and composable commonsense concepts, selected based on a thorough study of ontological and lexical resources. Our translation targetsCLibstates(i.e.,fluents) andactions. 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 itsCLibcounterpart: (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...

The 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. Atheoryconsis...

The paper presents the syntax and semantics of an action language ALM. The language is used for representation of knowledge about dynamic systems. It extends action language AL by allowing definitions of new objects (actions and fluents) in terms of other, previously defined, objects. This, together with the modular structure of the new language leads to more elegant and concise representations and facilitates creation of libraries of knowledge modules. The methodology of representing knowledge in ALM is illustrated by a number of examples.

Thanks to a number of efficient implementations, the use of logic formalisms for problem-solving has been increased in several real-world domains. This is the case, for instance, of action languages, such as planning domain definition language (PDDL), or answer set programming (ASP), which is a well-established declarative problem-solving paradigm that became widely used in AI and recognized as a powerful tool for knowledge representation and reasoning (KRR). As the application scenarios widened, the need for proper development tools and interoperability mechanisms for easing interaction and integration between declarative logic-based systems and external systems clearly emerged. In this work, we present a framework for integrating the KRR capabilities of, possibly more than one, declarative formalisms into generic applications developed by means of different programming paradigms. We show the use of the framework by illustrating proper specializations for two formalisms, namely ASP and PDDL, along with specializations for some relevant systems over different platforms, including the mobile setting.

We address the issue of incorporating domain-specific preferences in planning systems, where a preference may be seen as a "soft" constraint that it is desirable, but not necessary, to satisfy. To this end, we identify two types of preferences, choice preferences that give a preference over which formulas (typically subgoals) to establish, and temporal preferences, which specify a desirable ordering on the establishment of formulas. Preferences may be constructed from actions or fluents but, as we show, this distinction is immaterial. In fact, we allow preferences on arbitrary formulas build from action and fluent names. These preference orderings induce preference ordering on resulting plans, the maximal elements of which yield the preferred plans. We argue that the approach is general and flexible; as well, it handles conditional preferences. Our framework is developed in the context of transition systems; hence, it is applicable to a large number of different action languages, including the well-known language C. Furthermore, our results are applicable to general planning formalisms.

We describe an approach for compiling dynamic preferences into logic programs under the answer set semantics. An ordered logic program is an extended logic program in which rules are named by unique terms, and in which preferences among rules are given by a set of atoms of the form s ≺ t where s and t are names. An ordered logic program is transformed into a second, regular, extended logic program wherein the preferences are respected, in that the answer sets obtained in the transformed theory correspond with the preferred answer sets of the original theory. Our approach allows the specification of static orderings (in which preferences are external to a logic program), as well as dynamic orderings (in which preferences can appear within a program), and orderings on sets of rules. In large part then, we are interested in describing a general methodology for uniformly incorporating preference information in a logic program. Since the result of our translation is an extended logic program, we can make use of existing implementations, such as dlv and smodels. To this end, we have developed a compiler, available on the web, as a front-end for these programming systems.

The formal similarity between possibility theory and formal concept analysis, made ten years ago, has suggested the introduction in the latter setting of the counterpart of possibilistic operators, which were ignored before. These new operators can be related to the basic operator of formal concept analysis by a triple use of negations on the contexts, on the set-valued arguments and on the obtained results, and lead to consider new compositions worth of interest. They enable us to complete the Guigues-Duquenne basis with rules having disjunctive conclusions. Besides, the approach can be naturally generalized to incomplete contexts and then to uncertain context where uncertainty is graded.

Rule-based declarative formalisms enjoy several advantages when compared with imperative solutions, especially when dealing with AI-based application development: solid theoretical bases, no need for algorithm design or coding, explicit and easily modifiable knowledge bases, executable declarative specifications, fast prototyping, quick error detection, modularity. For these reasons, ways for combining declarative paradigms, such as Answer Set Programming (ASP), with traditional ones have been significantly studied in the recent years; there are however relevant contexts, in which this road is unexplored, such as development of real-time games. In such a setting, the strict requirements on reaction times, the presence of computer-human interactivity and a generally increased impedance between the two development paradigms make the task nontrivial. In this work we illustrate how to embed rulebased reasoning modules into the well-known Unity game development engine. To this end, we present an extension of EmbASP, a framework to ease the integration of declarative formalisms with generic applications. We prove the viability of our approach by developing a proof-of-concept Unity game that makes use of ASP-based AI modules.

Answer Set Programming (ASP) is a well-established declarative problem solving paradigm which became widely used in AI and recognized as a powerful tool for knowledge representation and reasoning (KRR), especially for its high expressiveness and the ability to deal also with incomplete knowledge. Recently, thanks to the availability of a number of robust and efficient implementations, ASP has been increasingly employed in a number of different domains, and used for the development of industrial-level and enterprise applications. This made clear the need for proper development tools and interoperability mechanisms for easing interaction and integration with external systems in the widest range of real-world scenarios, including mobile applications and educational contexts. In this work we present a framework for integrating the KRR capabilities of ASP into generic applications. We show the use of the framework by illustrating proper specializations for some relevant ASP systems over different platforms, including the mobile setting; furthermore, the potential of the framework for educational purposes is illustrated by means of the development of several ASP-based applications.

Designing and implementing AI in games is an interesting, yet complex task. This paper briefly presents some applications that make use of Answer Set Programming for such a task, and show some advantages of declarative programming frameworks against imperative (algorithmic) approaches while dealing with knowledge representation and reasoning: solid theoretical bases, no need for algorithm design or coding, explicit (and thus easily modifiable/upgradeable) knowledge representation, declarative specifications which are already executable, very fast prototyping, quick error detection, modularity.