ALLIANCE is a software architecture that fa- cilitates the fault tolerant cooperative control of teams of heterogeneous mobile robots performing missions composed of loosely coupled subtasks that may have ordering dependencies. ALLIANCE allows teams of robots, each of which possesses a variety of high-level functions that it can perform during a mission, to individually select appropriate actions throughout the mission based on the requirements of the mission, the activities of other robots, the current environmental conditions, and the robot's own internal states. ALLIANCE is a fully distributed, behavior-based architecture that incorporates the use of mathematically-modeled motivations (such as impatience and acquiescence) within each robot to achieve adaptive action selection. Since cooperative robotic teams usually work in dynamic and unpredictable environments, this software architecture allows the robot team members to respond robustly, reliably, flexibly, and coherently to unexpected environmental changes and modifications in the robot team that may occur due to mechanical failure, the learning of new skills, or the addition or removal of robots from the team by human intervention. The feasibility of this architecture is demonstrated in an implementation on a team of mobile robots performing a laboratory version of hazardous waste cleanup.

The evolution of language implies the parallel evolution of an ability to respond appropriately to signals (language understanding) and an ability to produce the appropriate signals in the appropriate circumstances (language production). When linguistic signals are produced to inform other individuals, individuals that respond appropriately to these signals may increase their reproductive chances but it is less clear what is the reproductive advantage for the languages producers. We present simulations in which populations of neural networks living in an environment evolve a simple language with an informative function. Signals are produced to help other individuals to categorize edible and poisonous mushrooms in order to decide whether to approach or avoid encountered mushrooms. Language production, while not under direct evolutionary pressure, evolves as a by-product of the independently evolving perceptual ability to categorize mushrooms. Keywords: Language evolution, Ge...

An approach to develop new game playing strategies based on artificial evolution of neural networks is presented. Evolution was directed to discover strategies in Othello against a random-moving opponent and later against an ff-fi search program. The networks discovered first a standard positional strategy, and subsequently a mobility strategy, an advanced strategy rarely seen outside of tournaments. The latter discovery demonstrates how evolutionary neural networks can develop novel solutions by turning an initial disadvantage into an advantage in a changed environment. 1 Introduction Game playing is one of the oldest and most extensively studied areas of artificial intelligence. Games require sophisticated intelligence in a well-defined problem where success is easily measured. Games have therefore proven to be important domains for studying problem solving techniques. Most research in game playing has centered on creating deeper searches through the possible game scenarios. Deeper ...

Genetic algorithms are computational models of evolution that play a central role in many artificial-life models. We review the history and current scope of research on genetic algorithms in artificial life, using illustrative examples in which the genetic algorithm is used to study how learning and evolution interact, and to model ecosystems, immune system, cognitive systems, and social systems. We also outline a number of open questions and future directions for genetic algorithms in artificial-life research.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii I Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 A. Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1. The #arti#cial life bridge" . . . . . . . . . . . . . . . . . . . . . . 1 2. From nature to technology . . . . . . . . . . . . . . . . . . . . . 3 3. From technology to nature . . . . . . . . . . . . . . . . . . . . . 4 B. Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 II Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 A. Background: Machine learning . . . . . . . . . . . . . . . . . . . . . 10 1. Evolutionary algorithms . . . . . . . . . . . . . . . . . . . . . . . 11 2. Endogenous #tness . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3. Reinforcement learning . . . . . . . . . . . . . . . . . . . . . . . 18 B. Local selection . . . . . . . . . . . . . . . . . . . . . . . . . . ...

Models of the world can take many shapes. In this paper, we will discuss how groups of autonomous robots learn languages that can be used as a means for modeling the environment. The robots have already learned simple languages for communication of task instructions. These languages are adaptable under changing situations � i.e. once the robots learn a language, they are able to learn new concepts and update old concepts. In this prior work, reinforcement learning using a human instructor provides the motivation for communication. In current work, the world will be the motivation for learning languages. Since the languages are grounded in the world, they can be used to talk about the world � in e ect, the language is the means the robots use to model the world. This paper will explore the issues of learning to communicate solely through environment motivation. Additionally, we will discuss the possible uses of these languages for interacting with the world. 1