356 research outputs found

    cc-Golog: Towards More Realistic Logic-Based Robot Controllers

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    High-level robot controllers in realistic domains typically deal with processes which operate concurrently, change the world continuously, and where the execution of actions is event-driven as in ``charge the batteries as soon as the voltage level is low''. While non-logic-based robot control languages are well suited to express such scenarios, they fare poorly when it comes to projecting, in a conspicuous way, how the world evolves when actions are executed. On the other hand, a logic-based control language like \congolog, based on the situation calculus, is well-suited for the latter. However, it has problems expressing event-driven behavior. In this paper, we show how these problems can be overcome by first extending the situation calculus to support continuous change and event-driven behavior and then presenting \ccgolog, a variant of \congolog which is based on the extended situation calculus. One benefit of \ccgolog is that it narrows the gap in expressiveness compared to non-logic-based control languages while preserving a semantically well-founded projection mechanism

    Constraint integration and violation handling for BPEL processes

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    Autonomic, i.e. dynamic and fault-tolerant Web service composition is a requirement resulting from recent developments such as on-demand services. In the context of planning-based service composition, multi-agent planning and dynamic error handling are still unresolved problems. Recently, business rule and constraint management has been looked at for enterprise SOA to add business flexibility. This paper proposes a constraint integration and violation handling technique for dynamic service composition. Higher degrees of reliability and fault-tolerance, but also performance for autonomously composed WS-BPEL processes are the objectives

    Logic-Based Specification Languages for Intelligent Software Agents

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    The research field of Agent-Oriented Software Engineering (AOSE) aims to find abstractions, languages, methodologies and toolkits for modeling, verifying, validating and prototyping complex applications conceptualized as Multiagent Systems (MASs). A very lively research sub-field studies how formal methods can be used for AOSE. This paper presents a detailed survey of six logic-based executable agent specification languages that have been chosen for their potential to be integrated in our ARPEGGIO project, an open framework for specifying and prototyping a MAS. The six languages are ConGoLog, Agent-0, the IMPACT agent programming language, DyLog, Concurrent METATEM and Ehhf. For each executable language, the logic foundations are described and an example of use is shown. A comparison of the six languages and a survey of similar approaches complete the paper, together with considerations of the advantages of using logic-based languages in MAS modeling and prototyping.Comment: 67 pages, 1 table, 1 figure. Accepted for publication by the Journal "Theory and Practice of Logic Programming", volume 4, Maurice Bruynooghe Editor-in-Chie

    Artificial Intelligence and Systems Theory: Applied to Cooperative Robots

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    This paper describes an approach to the design of a population of cooperative robots based on concepts borrowed from Systems Theory and Artificial Intelligence. The research has been developed under the SocRob project, carried out by the Intelligent Systems Laboratory at the Institute for Systems and Robotics - Instituto Superior Tecnico (ISR/IST) in Lisbon. The acronym of the project stands both for "Society of Robots" and "Soccer Robots", the case study where we are testing our population of robots. Designing soccer robots is a very challenging problem, where the robots must act not only to shoot a ball towards the goal, but also to detect and avoid static (walls, stopped robots) and dynamic (moving robots) obstacles. Furthermore, they must cooperate to defeat an opposing team. Our past and current research in soccer robotics includes cooperative sensor fusion for world modeling, object recognition and tracking, robot navigation, multi-robot distributed task planning and coordination, including cooperative reinforcement learning in cooperative and adversarial environments, and behavior-based architectures for real time task execution of cooperating robot teams
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