Daniela Inclezan

Some properties of system descriptions of

Abstract The paper discusses some properties of system descriptions in action language – a recent extension of action language (also known as ) by defined fluents. We give a sufficient condition guaranteeing that states of an system description are fully determined by statics and inertial fluents. In system descriptions satisfying this condition, defined fluents simply facilitate the description of dynamic domains; they are not essential and can be eliminated. We use our sufficient condition to identify a common core of action languages and . This is an expansion of the work of , who identified a common core of languages and . The results presented in this paper are based on the close relationship between action languages and the theory of logic programs under the answer set semantics.

Modular Action Language ALM

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 domains 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. To appear in Theory and Practice of Logic Programming (TPLP).

An Application of ASP to the Field of Second Language Acquisition

This paper explores the contributions of Answer Set Programming ASP to the study of an established theory from the field of Second Language Acquisition: Input Processing. The theory describes default strategies that learners of a second language use in extracting meaning out of a text, based on their knowledge of the second language and their background knowledge about the world. We formalized this theory in ASP, and as a result we were able to determine opportunities for refining its natural language description, as well as directions for future theory development. We applied our model to automating the prediction of how learners of English would interpret sentences containing the passive voice. We present a system, PIas, that uses these predictions to assist language instructors in designing teaching materials.

Automated Inference of Socio-Cultural Information from Natural Language Conversations

We discuss a methodology for extracting socio-cultural information from transcripts of natural language conversations. The methodology is applicable to a wide variety of languages. We use Russian and Tamil for illustration. The extracted socio-cultural information pertains to the nature of the relationship between the participants in the interaction. We concentrate on the information implicit in the use of terms that refer to people (pronouns, terms of address etc.). We have constructed an AnsProlog theory of the use of these language indicators in Russian and also in Tamil. It is this theory that looks at the usages of these indicators in the conversation in question and produces information about the relationships of the participants in the conversation.

Viewpoint: a critical view on smart cities and AI

AI developments on smart cities, if not critical, risk making a flawed urban model more efficient. Instead, we suggest that AI should challenge the mainstream techno-optimistic approach to solving urban problems by dialoguing with other academic fields, questioning the dominant urban paradigm, and creating transformative solutions. We claim that doing differently, rather than doing better, may be smarter for cities and the common good.

Recognizing Social Constructs from Textual Conversation

In this paper we present our work on recognizing high level social constructs such as Leadership and Status from textual conversation using an approach that makes use of the background knowledge about social hierarchy and integrates statistical methods and symbolic logic based methods. We use a stratified approach in which we first detect lower level language constructs such as politeness, command and agreement that help us to infer intermediate constructs such as deference, closeness and authority that are observed between the parties engaged in conversation. These intermediate constructs in turn are used to determine the social constructs Leadership and Status. We have implemented this system successfully in both English and Korean languages and achieved considerable accuracy.

An application of answer set programming to the field of second language acquisition

This paper explores the contributions of Answer Set Programming (ASP) to the study of an established theory from the field of Second Language Acquisition: Input Processing. The theory describes default strategies that learners of a second language use in extracting meaning out of a text, based on their knowledge of the second language and their background knowledge about the world. We formalized this theory in ASP, and as a result we were able to determine opportunities for refining its natural language description, as well as directions for future theory development. We applied our model to automating the prediction of how learners of English would interpret sentences containing the passive voice. We present a system, PIas, that uses these predictions to assist language instructors in designing teaching materials. To appear in Theory and Practice of Logic Programming (TPLP).

An Answer Set Prolog formalization of shikake principles and examples

AbstractShikake is a design approach that proposes solving problems by inducing spontaneous behavior, rather than by relying on the use of extensive resources or expertise. This paper contributes to the study of Shikake principles and examples by describing a methodology for their formalization in the declarative, logic-based language of Answer Set Prolog (ASP). Modeling qualitative theories and principles such as Shikake in the precise language of ASP can play a significant role in indicating possible areas for their future refinement and improvement, as shown here. Our formalization is used in creating a system, ShAsp, that can automatically determine if a design is a Shikake or not, as illustrated by two examples and one counterexample.

Understanding Restaurant Stories Using an ASP Theory of Intentions.

The paper describes an application of logic programming to story understanding. Substantial work in this direction has been done by Erik Mueller, who focused on texts about stereotypical activities (or scripts), in particular restaurant stories. His system performed well, but could not understand texts describing exceptional scenarios. We propose addressing this problem by using a theory of intentions developed by Blount, Gelfond, and Balduccini. We present a methodology in which we model scripts as activities and employ the concept of an intentional agent to reason about both normal and exceptional scenarios.

COREALMLIB: An ALM Library Translated from the Component Library.

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 CLIBs inference engine cannot handle planning nor postdiction. Under consideration for publication in TPLP.