Multi-Agent Planning and Coordination Under Resource Constraints

University dissertation from Rom : Italian National Research Council

Abstract: The research described in this thesis stems from ROBOCARE1, a three year research project aimed at developing software and robotic technology for providing intelligent support for elderly people. This thesis deals with two problems which have emerged in the course of the project’s development:Multi-agent coordination with scarce resources. Multi-agent planning is concerned with automatically devising plans or strategies for the coordinated enactment of concurrently executing agents. A common realistic constraint in applications which require the coordination of multiple agents is the scarcity of resources for execution. In these cases, concurrency is affected by limited capacity resources, the presence of which modifies the structure of the planning/coordination problem. Specifically, the first part of this thesis tackles this problem in two contexts, namely when planning is carried out centrally (planning from first principles), and in the context of distributed multi-agent coordination.Domain modeling for scheduling applications. It is often the case that the products of research in AI problem solving are employed to develop applications for supporting human decision processes. Our experience in ROBOCARE as well as other domains has often called for the customization of prototypical software for real applications. Yet the gap between what is often a research prototype and a complete decision support system is seldom easy to bridge.The second part of the thesis focuses on this issue from the point of view of scheduling software deployment.Overall, this thesis presents three contributions within the two problems mentioned above. First, we address the issue of planning in concurrent domains in which the complexity of coordination is dominated by resource constraints. To this end, an integrated planning and scheduling architecture is presented and employed to explore the structural trademarks of multi-agent coordination problems in function of their resource-related characteristics. Theoretical and experimental analyses are carried out revealing which planning strategies are most fit for achieving plans which prescribe efficient coordination subject to scarce resources.We then turn our attention to distributed multi-agent coordination techniques (specifically, a distributed constraint optimization (DCOP) reduction of the coordination problem). Again, we consider the issue of achieving coordinated action in the presence of limited resources. Specifically, resource constraints impose n-ary relations among tasks. In addition, as the number of n-ary relations due to resource contention are exponential in the size of the problem, they cannot be extensionally represented in the DCOP representation of the coordination problem. Thus, we propose an algorithm for DCOP which retains the capability to dynamically post n-ary constraints during problem resolution in order to guarantee resource-feasible solutions. Although the approach is motivated by the multi-agent coordination problem, the algorithm is employed to realize a general architecture for n-ary constraint reasoning and posting.Third, we focus on a somewhat separate issue stemming from ROBOCARE, namely a software engineering methodology for facilitating the process of customizing scheduling components in real-world applications. This work is motivated by the strong applicative requirements of ROBOCARE. We propose a software engineering methodology specific to scheduling technology development. Our experience in ROBOCARE as well as other application scenarios has fostered the development of a modeling framework which subsumes the process of component customization for scheduling applications. The framework aims to minimize the effort involved in deploying automated reasoning technology in practise, and is grounded on the use of a modeling language for defining how domain-level concepts are grounded into elements of a technology-specific scheduling ontology.