Characterizing Causal Action Theories and Their Implementations in Answer Set Programming: Action Languages B, C, and Beyond

AAAI Conferences

We consider a simple language for writing causal action theories, and postulate several properties for the state transition models of these theories. We then consider some possible embeddings of these causal action theories in some other action formalisms, and their implementations in logic programs with answer set semantics. In particular, we propose to consider what we call permissible translations from these causal action theories to logic programs. We identify two sets of properties, and prove that for each set, there is only one permissible translation, under strong equivalence, that can satisfy all properties in the set. As it turns out, for one set, the unique permissible translation is essentially the same as Balduccini and Gelfond's translation from Gelfond and Lifschitz's action language B to logic programs. For the other, it is essentially the same as Lifschitz and Turner's translation from the action language C to logic programs. This work provides a new perspective on understanding, evaluating and comparing action languages by using sets of properties instead of examples. It will be interesting to see if other action languages can be similarly characterized, and whether new action formalisms can be defined using different sets of properties.


Mapping Action Language BC to Logic Programs: A Characterization by Postulates

AAAI Conferences

We have earlier shown that the standard mappings from action languages B and C to logic programs under answer set semantics can be captured by sets of properties on transition systems. In this paper, we consider action language BC and show that a standard mapping from BC action descriptions to logic programs can be similarly captured when the action rules in the descriptions do not have consistency conditions.


Towards Answer Set Prolog Based Architectures for Intelligent Agents

AAAI Conferences

An important research area in the field of AI is the design of intelligent agents acting in a changing environment. Research in this area has led to development of agent architectures that support various tasks such as planning, diagnosis, learning etc. One such architecture is based on the agent repeatedly executing the observe-think-act-loop (Baral and Gelfond 2000; Kowalski and Sadri 1999). This architecture is applicable if the world including the agent and its environment is viewed as a transition diagram whose states correspond to possible physical states of the world and whose arcs are labeled by actions. One of the approaches to describing these diagrams is a theory based on action languages - formal models of parts of natural language used for reasoning about actions and their effects (Gelfond and Lifschitz 1998).


Action Language BC: Preliminary Report

AAAI Conferences

The action description languages B and C have significant common core. Nevertheless, some expressive possibilities of B are difficult or impossible to simulate in C, and the other way around. The main advantage of B is that it allows the user to give Prolog-style recursive definitions, which is important in applications. On the otherhand, B solves the frame problem by incorporating the commonsense law of inertia in its semantics, which makes it difficult to talk about fluents whose behavior is described by defaults other than inertia. In C and in its extension C+, the inertia assumption is expressed by axioms that the user is free to include or not to include, and other defaults can be postulated as well. This paper defines a new action description language, called BC, that combines the attractive features of B and C. Examples of formalizing commonsense domains discussed in the paper illustrate the expressive capabilities of BC and the use of answer set solvers for the automation of reasoning about actions described inthis language.


Answer set programming as the basis for a Homeland Security QAS

AAAI Conferences

In this paper we discuss the applicability of the knowledge representation and reasoning language AnsProlog (Answer Set Programming) for the design and implementation of a query answering system (QAS) for homeland security. We discuss our work to date on using AnsProlog to axiomatize the travel domain. We illustrate how it can be used to represent defaults, causal relations, and other types of commonsense knowledge needed to properly answer nontrivial questions about this domain.