We describe mobile robots engaged in a cooperative task that requires communication. The robots are initially given a fixed but uninterpreted vocabulary for communication. In attempting to perform their task, the robots learn a private communication language. Different meanings for vocabulary elements are learned in different runs of the experiment. As circumstances change, the robots adapt their language to allow continued success at their task. 1 Introduction In this paper, we investigate the evolution of simple communication protocols among nonverbal subjects engaged in cooperative tasks. Gregarious animals, small children, and even adult humans lacking common language engage in such activity routinely. Grunts, gestures, and other nonverbal signals take on mutually agreed-upon meanings in the context of cooperative tasks. "Follow me," "Look out!" and "Raise your end of the table higher" can all be conveyed without previously agreed-upon language. Satisfactory completion of cooperat...

We are implementing a control system for a discrete manufacturing environment that partitions tasks using a negotiation protocol like the contract net described by Smith and Davis [24,25,26,3]. The application domain differs in interesting ways from those to which contract nets have previously been applied. This report

For many distributed applications, it is not sufficient for programs to be logically correct. In addition, they must satisfy various timing constraints. This paper discusses primitives that support the construction of distributed real-time programs. Our discussion is focused in two are&: ~ specification and communication. To allow the specifications of timing constraints. we introduce the lanrmsge-- constructs for defining temporal scope and specifying meflqsge deadline. We also identify communication prmtives needed for real-time pkgremming. he issues underlying the selection of the primitives are explained, including handling of timiig exceptions. The primitives will eventually be provided as part of a ditributed programming system that will be used to construct ditributed multi-sensory systems.

To increase speed and reliability of operation, multiple computers are replacing uniprocessors and wired-logic controllers in modern robots and industrial control systems. However, performance increases are not attained by such hardware alone. The operating software controlling the robots or control systems must exploit the possible parallelism of various control tasks in order to perform the necessary computations within given real-time and reliability constraints. Such software consists of both control programs written by application programmers and operating system software offering means of task scheduling, intertask communication, and device control. The Generalized Executive for real-time Multiprocessor applications (GEM) is an operating system that addresses several requirements of operating software. First, when using GEM, programmers can select one of two different types of tasks differing in size, called processes and microprocesses. Second, the scheduling calls offered by GEM permit the implementation of several models of task interaction. Third, GEM supports multiple models of communication with a parameterized communication mechanism. Fourth, GEM is closely coupled to prototype real-time programming environments that provide programming support for the models of computation offered by the operating system. GEM is being used on a multiprocessor with robotics application software of substantial size and complexity.

Abstract. Reactive multiagent systems are shown to coevolve with explicit communication and cooperative behavior to solve lattice formation tasks. Comparable agents that lack the ability to communicate and cooperate are shown to be unsuccessful in solving the same tasks. The agents without any centralized supervision develop a communication protocol with a mutually agreed upon signaling scheme to share sensor data between a pair of individuals. The control system for these agents consists of identical cellular automata handling communication, cooperation and motion subsystems. Shannon’s entropy function was used as a fitness evaluator to evolve the desired cellular automata. The results are derived from computer simulations. 1

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