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

Synthesis for LTL and LDL on Finite Traces

In this paper, we study synthesis from logical specifications over finite traces expressed in LTLf and its extension LDLf. Specifically, in this form of synthesis, propositions are partitioned in controllable and uncontrollable ones, and the synthesis task consists of setting the controllable propositions over time so that, in spite of how the value of the uncon- trollable ones changes, the specification is fulfilled. Conditional planning in presence of declarative and procedural trajectory constraints is a special case of this form of synthesis. We characterize the problem computationally as 2EXPTIME-complete and present a sound and complete synthesis technique based on DFA (reachability) games

A belief-desire-intention architechture with a logic-based planner for agents in stochastic domains

This dissertation investigates high-level decision making for agents that are both goal and utility driven. We develop a partially observable Markov decision process (POMDP) planner which is an extension of an agent programming language called DTGolog, itself an extension of the Golog language. Golog is based on a logic for reasoning about action—the situation calculus. A POMDP planner on its own cannot cope well with dynamically changing environments and complicated goals. This is exactly a strength of the belief-desire-intention (BDI) model: BDI theory has been developed to design agents that can select goals intelligently, dynamically abandon and adopt new goals, and yet commit to intentions for achieving goals. The contribution of this research is twofold: (1) developing a relational POMDP planner for cognitive robotics, (2) specifying a preliminary BDI architecture that can deal with stochasticity in action and perception, by employing the planner.ComputingM. Sc. (Computer Science

Reasoning about Imperfect Information Games in the Epistemic Situation Calculus

Approaches to reasoning about knowledge in imperfect information games typically involve an exhaustive description of the game, the dynamics characterized by a tree and the incompleteness in knowledge by information sets. Such specifications depend on a modeler's intuition, are tedious to draft and vague on where the knowledge comes from. Also, formalisms proposed so far are essentially propositional, which, at the very least, makes them cumbersome to use in realistic scenarios. In this paper, we propose to model imperfect information games in a new multi-agent epistemic variant of the situation calculus. By using the concept of only-knowing, the beliefs and non-beliefs of players after any sequence of actions, sensing or otherwise, can be characterized as entailments in this logic. We show how de re vs. de dicto belief distinctions come about in the framework. We also obtain a regression theorem for multi-agent beliefs, which reduces reasoning about beliefs after actions to reasoning about beliefs in the initial situation

Plan Projection, Execution, and Learning for Mobile Robot Control

Most state-of-the-art hybrid control systems for mobile robots are decomposed into different layers. While the deliberation layer reasons about the actions required for the robot in order to achieve a given goal, the behavioral layer is designed to enable the robot to quickly react to unforeseen events. This decomposition guarantees a safe operation even in the presence of unforeseen and dynamic obstacles and enables the robot to cope with situations it was not explicitly programmed for. The layered design, however, also leaves us with the problem of plan execution. The problem of plan execution is the problem of arbitrating between the deliberation- and the behavioral layer. Abstract symbolic actions have to be translated into streams of local control commands. Simultaneously, execution failures have to be handled on an appropriate level of abstraction. It is now widely accepted that plan execution should form a third layer of a hybrid robot control system. The resulting layered architectures are called three-tiered architectures, or 3T architectures for short. Although many high level programming frameworks have been proposed to support the implementation of the intermediate layer, there is no generally accepted algorithmic basis for plan execution in three-tiered architectures. In this thesis, we propose to base plan execution on plan projection and learning and present a general framework for the self-supervised improvement of plan execution. This framework has been implemented in APPEAL, an Architecture for Plan Projection, Execution And Learning, which extends the well known RHINO control system by introducing an execution layer. This thesis contributes to the field of plan-based mobile robot control which investigates the interrelation between planning, reasoning, and learning techniques based on an explicit representation of the robot's intended course of action, a plan. In McDermott's terminology, a plan is that part of a robot control program, which the robot cannot only execute, but also reason about and manipulate. According to that broad view, a plan may serve many purposes in a robot control system like reasoning about future behavior, the revision of intended activities, or learning. In this thesis, plan-based control is applied to the self-supervised improvement of mobile robot plan execution

Learning plan selection for BDI agent systems

Belief-Desire-Intention (BDI) is a popular agent-oriented programming approach for developing robust computer programs that operate in dynamic environments. These programs contain pre-programmed abstract procedures that capture domain know-how, and work by dynamically applying these procedures, or plans, to different situations that they encounter. Agent programs built using the BDI paradigm, however, do not traditionally do learning, which becomes important if a deployed agent is to be able to adapt to changing situations over time. Our vision is to allow programming of agent systems that are capable of adjusting to ongoing changes in the environment’s dynamics in a robust and effective manner. To this end, in this thesis we develop a framework that can be used by programmers to build adaptable BDI agents that can improve plan selection over time by learning from their experiences. These learning agents can dynamically adjust their choice of which plan to select in which situation, based on a growing understanding of what works and a sense of how reliable this understanding is. This reliability is given by a perceived measure of confidence, that tries to capture how well-informed the agent’s most recent decisions were and how well it knows the most recent situations that it encountered. An important focus of this work is to make this approach practical. Our framework allows learning to be integrated into BDI programs of reasonable complexity, including those that use recursion and failure recovery mechanisms. We show the usability of the framework in two complete programs: an implementation of the Towers of Hanoi game where recursive solutions must be learnt, and a modular battery system controller where the environment dynamics changes in ways that may require many learning and relearning phases