Abstraction in situation calculus action theories

We develop a general framework for agent abstraction based on the situation calculus and the ConGolog agent programming language. We assume that we have a high-level specification and a low-level specification of the agent, both repre- sented as basic action theories. A refinement mapping specifies how each high-level action is implemented by a low- level ConGolog program and how each high-level fluent can be translated into a low-level formula. We define a notion of sound abstraction between such action theories in terms of the existence of a suitable bisimulation between their respective models. Sound abstractions have many useful properties that ensure that we can reason about the agent’s actions (e.g., executability, projection, and planning) at the abstract level, and refine and concretely execute them at the low level. We also characterize the notion of complete abstraction where all actions (including exogenous ones) that the high level thinks can happen can in fact occur at the low level

Bounded Situation Calculus Action Theories

In this paper, we investigate bounded action theories in the situation calculus. A bounded action theory is one which entails that, in every situation, the number of object tuples in the extension of fluents is bounded by a given constant, although such extensions are in general different across the infinitely many situations. We argue that such theories are common in applications, either because facts do not persist indefinitely or because the agent eventually forgets some facts, as new ones are learnt. We discuss various classes of bounded action theories. Then we show that verification of a powerful first-order variant of the mu-calculus is decidable for such theories. Notably, this variant supports a controlled form of quantification across situations. We also show that through verification, we can actually check whether an arbitrary action theory maintains boundedness.Comment: 51 page

Abstraction in situation calculus action theories

We develop a general framework for agent abstraction based on the situation calculus and the ConGolog agent programming language. We assume that we have a high-level specification and a low-level specification of the agent, both repre- sented as basic action theories. A refinement mapping specifies how each high-level action is implemented by a low- level ConGolog program and how each high-level fluent can be translated into a low-level formula. We define a notion of sound abstraction between such action theories in terms of the existence of a suitable bisimulation between their respective models. Sound abstractions have many useful properties that ensure that we can reason about the agent’s actions (e.g., executability, projection, and planning) at the abstract level, and refine and concretely execute them at the low level. We also characterize the notion of complete abstraction where all actions (including exogenous ones) that the high level thinks can happen can in fact occur at the low level

LTL Verification of Online Executions with Sensing in Bounded Situation Calculus

Abstract. We look at agents reasoning about actions from a firstperson perspective. The agent has a representation of world as situation calculus action theory. It can perform sensing actions to acquire information. The agent acts “online”, i.e., it performs an action only if it is certain that the action can be executed, and collects sensing results from the actual world. When the agent reasons about its future actions, it indeed considers that it is acting online; however only possible sensing values are available. The kind of reasoning about actions we consider for the agent is verifying a first-order (FO) variant (without quantification across situations) of linear time temporal logic (LTL). We mainly focus on bounded action theories, where the number of facts that are true in any situation is bounded. The main results of this paper are: (i) possible sensing values can be based on consistency if the initial situation description is FO; (ii) for bounded action theories, progression over histories that include sensing results is always FO; (iii) for bounded theories, verifying our FO LTL against online executions with sensing is decidable.

ElGolog: A High-Level Programming Language with Memory of the Execution History

Most programming languages only support tests that refer exclusively to the current state. This applies even to high-level programming languages based on the situation calculus such as Golog. The result is that additional variables/fluents/data structures must be introduced to track conditions that the pro- gram uses in tests to make decisions. In this paper, drawing inspiration from McCarthy’s Elephant 2000, we propose an extended version of Golog, called ElGolog, that supports rich tests about the execution history, where tests are expressed in a first-order variant of two-way linear dynamic logic that uses ElGolog programs with converse. We show that in spite of rich tests, ElGolog shares key features with Golog, including a sematics based on macroexpansion into situation calculus formulas, upon which regression can still be applied. We also show that like Golog, our extended language can easily be implemented in Prolog

Bounded Situation Calculus Action Theories and Decidable Verification

Abstract We define a notion of bounded action theory in the situation calculus, where the theory entails that in all situations, the number of ground fluent atoms is bounded by a constant. Such theories can still have an infinite domain and an infinite set of states. We argue that such theories are fairly common in applications, either because facts do not persist indefinitely or because one eventually forgets some facts, as one learns new ones. We discuss various ways of obtaining bounded action theories. The main result of the paper is that verification of an expressive class of first-order µ-calculus temporal properties in such theories is in fact decidable

IG-JADE-PKSlib: An Agent-Based Framework for Advanced Web Service Composition and Provisioning

In this paper we describe an agent-based infrastructure and toolkit to develop inter-operable, intelligent, multiagent systems for Web service composition (WSC) and provisioning. Our toolkit is realized through an interface library (IG-JADE-PKSlib) that combines state of the art agent-based and planning technologies (i.e., the IndiGolog model-based agent programming language, the JADE agent platform, and the PKS planning system). We show that each of these tools has its strengths and weaknesses, but combined together, they provide a very powerful toolkit. We argue that this infrastructure is particularly well suited for developing next generation Web services (WS) applications

Modeling Mental States in Agent-Oriented Requirements Engineering

Abstract. This paper describes an agent-oriented requirements engineering approach that combines informal i * models with formal specifications in the multiagent system specification formalism CASL. This allows the requirements engineer to exploit the complementary features of the frameworks. i * can be used to model social dependencies between agents and how process design choices affect the agents ’ goals. CASL can be used to model complex processes formally. We introduce an intermediate notation to support the mapping between i * models and CASL specifications. In the combined i*-CASL framework, agents ’ goals and knowledge are represented as their mental states, which allows for the formal analysis and verification of, among other things, complex agent interactions and incomplete knowledge. Our models can also serve as high-level specifications for multiagent systems.