ARBEITSBEREICH WISSENSBASIERTE SYSTEME TEAM PROGRAMMING IN GOLOG UNDER PARTIAL OBSERVABILITY

Abstract. We present and explore the agent programming language TEAMGOLOG, which is a novel approach to programming a team of cooperative agents under partial observability. Every agent is associated with a partial control program in Golog, which is completed by the TEAMGOLOG interpreter in an optimal way by assuming a decision-theoretic semantics. The approach is based on the key concepts of a synchronization state and a communication state, which allow the agents to passively resp. actively coordinate their behavior, while keeping their belief states, observations, and activities invisible to the other agents. We show the practical usefulness of the TEAMGOLOG approach in a rescue simulated domain. We describe the algorithms behind the TEAMGOLOG interpreter and provide a prototype implementation. We also show through experimental results that the TEAMGOLOG approach outperforms a standard greedy one in the rescue simulated domain

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

Modelling causal reasoning

PhDAlthough human causal reasoning is widely acknowledged as an object of scientific enquiry, there is little consensus on an appropriate measure of progress. Up-to-date evidence of the standard method of research in the field shows that this method has been rejected at the birth of modern science. We describe an instance of the standard scientific method for modelling causal reasoning (causal calculators). The method allows for uniform proofs of three relevant computational properties: correctness of the model with respect to the intended model, full abstraction of the model (function) with respect to the equivalence of reasoning scenarios (input), and formal relations of equivalence and subsumption between models. The method extends and exploits the systematic paradigm [Handbook of Logic in Artificial Intelligence and Logic Programming, volume IV, p. 439-498, Oxford 1995] to fit with our interpretation of it. Using the described method, we present results for some major models, with an updated summary spanning seventy-two years of research in the field

Action Logic Programs: How to Specify Strategic Behavior in Dynamic Domains Using Logical Rules

We discuss a new concept of agent programs that combines logic programming with reasoning about actions. These agent logic programs are characterized by a clear separation between the specification of the agent’s strategic behavior and the underlying theory about the agent’s actions and their effects. This makes it a generic, declarative agent programming language, which can be combined with an action representation formalism of one’s choice. We present a declarative semantics for agent logic programs along with (two versions of) a sound and complete operational semantics, which combines the standard inference mechanisms for (constraint) logic programs with reasoning about actions