The RoboCare project focuses on the development of distributed systems in which software and robotic agents contribute to the common goal of generating active services in environments in which humans may need assistance and guidance, such as health care facilities. Thanks to recent technological advances, there are now many potential applications for robotics including multi-agent systems. The aim of the RoboCare Project 1 is to study issues and challenges involved in the design of systems for the care of the elderly that adopt both fixed and mobile heterogeneous agents. These agents can be robots, intelligent sensors or possibly even humans. RoboCare shares some aspects with other projects for the assistance of elderly people, such as Pearle, the mobile robotic assistant for the elderly Figure 1. Overall system structure: operator level system control and distributed environment. [9] and the Assisted Cognition Project [3], but addresses the particular goal of creating a heterogeneous multi-agent environment for generating user services.

Abstract. This paper attempts to analyze the issue of planning and scheduling integration from the point of view of information sharing. This concept is the basic bridging factor between the two realms of problem solving. In fact, the exchange of each solver’s point of view on the problem to be solved allows for a synergetic effort in the process of searching the space of states. In this work, we show how different solving strategies cooperate in this process by varying the degree of integration of the combined procedure. In particular, the analysis exposes the advantage of propagating sets of partial plans rather than reasoning on sequential state space representations. Also, we show how this is beneficial both to a component-based approach (in which information sharing occurs only once) and to more interleaved forms of integration. 1

In this paper we describe IPSS (Integrated Planning and Scheduling System), a domain independent solver that integrates an AI heuristic planner, that synthesizes courses of actions, with a constraint-based scheduler for reasoning about time and resources. IPSS is able to solve planning problems with time (precedence constraints, deadline, time windows, etc) and binary resource usage/consumption. Experimental results show that the contextual reasoning of the planner with the constraint-based solver allows to improve the total makespan on a set of problems characterized by multiple agents.