We introduce a framework, called "physicomimetics," that provides distributed control of large collections of mobile physical agents in sensor networks. The agents sense and react to virtual forces, which are motivated by natural physics laws. Thus, physicomimetics is founded upon solid scientific principles. Furthermore, this framework provides an effective basis for self-organization, fault-tolerance, and self-repair. Three primary factors distinguish our framework from others that are related: an emphasis on minimality (e.g., cost effectiveness of large numbers of agents implies a need for expendable platforms with few sensors), ease of implementation, and run-time efficiency. Examples are shown of how this framework has been applied to construct various regular geometric lattice configurations (distributed sensing grids), as well as dynamic behavior for perimeter defense and surveillance. Analyses are provided that facilitate system understanding and predictability, including both qualitative and quantitative analyses of potential energy and a system phase transition. Physicomimetics has been implemented both in simulation and on a team of seven mobile robots. Specifics of the robotic embodiment are presented in the paper.

We introduce a novel framework called "artificial physics", which provides distributed control of large collections of agents. The agents react to artificial forces that are motivated by natural physical laws. This framework provides an effective mechanism for achieving self-assembly, fault-tolerance, and self-repair. Examples are shown for various regular geometric configurations of agents. A further example demonstrates that self-assembly via distributed control can also perform distributed computation. 1.

) Vineet Gupta Radha Jagadeesan Prakash Panangaden y vgupta@mail.arc.nasa.gov radha@cs.luc.edu prakash@cs.mcgill.ca Caelum Research Corporation Dept. of Math. and Computer Sciences School of Computer Science NASA Ames Research Center Loyola University--Lake Shore Campus McGill University Moffett Field CA 94035, USA Chicago IL 60626, USA Montreal, Quebec, Canada Abstract This paper describes a stochastic concurrent constraint language for the description and programming of concurrent probabilistic systems. The language can be viewed both as a calculus for describing and reasoning about stochastic processes and as an executable language for simulating stochastic processes. In this language programs encode probability distributions over (potentially infinite) sets of objects. We illustrate the subtleties that arise from the interaction of constraints, random choice and recursion. We describe operational semantics of these programs (programs are run by sampling random choices), deno...

In this paper, we combine two frameworks in the context of an important application. The first framework, called “artificial physics, ” is described in detail in a companion paper by Spears and Gordon [13]. The purpose of artificial physics is the distributed spatial control of large collections of mobile physical agents. The agents can be composed into geometric patterns (e.g., to act as a sensing grid) by having them sense and respond to local artificial forces that are motivated by natural physics laws. The purpose of the second framework is global monitoring of the agent formations developed with artificial physics. Using only limited global information, the monitor checks that the desired geometric pattern emerges over time as expected. If there is a problem, the global monitor steers the agents to self-repair. Our combined approach of local control through artificial physics, global monitoring, and “steering ” for self-repair is implemented and tested on a problem where multiple agents form a hexagonal lattice pattern. 1.

Autonomic computing aims to deal with the complexity of todays systems by letting the system handle the complexity autonomously. This is a very hard and challenging domain because current systems are complex, distributed, interconnected and rapidly changing systems. We firmly belief that a main challenge in conquering autonomic systems is the integration of three existing research communities: the multi-agent systems community allows natural modelling of the system and explicitly considers autonomous behaviour and distributed interaction, dynamical systems theory allows analysis of the dynamics of these models and the decentralised control community can use insights gathered from the analysis to create decentralised control mechanisms to control the dynamics of autonomic systems. This paper describes this generic perspective on autonomic computing, gives an overview of the relevant work done in each community and describes the contribution of each community in conquering autonomic computing.