Discrete Event Systems (DES) are a special type of dynamic system. The "state" of these systems change at discrete instants in time and the term "event" represents the occurrence of discontinuous change (at possibly unknown intervals). Different Discrete Event Systems models are currently used for specification, verification, synthesis as well as for analysis and evaluation of different qualitative and quantitative properties of existing physical systems. The focus of this paper is the presentation of the automata and formal language model for DES introduced by Ramadge and Wonham and its application to the domain of mobile manipulator/observer agents. We demonstrate the feasibility of the DES framework for modeling, analysis and synthesis of some visually guided behaviors of agents engaged in navigational tasks and address synchronization issues between different components of the system. The use of DES formalism allows us to synthesize complex behaviors in a systematic fashion and gua...

Using a simple computational model of visual perception and locomotion, obstacle avoidance behavior can emerge from evolution under selection pressure from an appropriate fitness measure. The Genetic Programming paradigm is used to model evolution. Both the structure of the visual sensor array, and the mapping from sensor data to motor action is determined by an evolved control program. The motor model assumes an innate constant forward velocity and limited steering. The agent can avoid collisions only by effective steering. Fitness is based on the number of simulation steps the agent can run before colliding with an obstacle.

Robust behavioral control programs for a simulated 2d vehicle can be constructed by artificial evolution. Corridor following serves here as an example of a behavior to be obtained through evolution. A controller's fitness is judged by its ability to steer its vehicle along a collision free path through a simple corridor environment. The controller's inputs are noisy range sensors and its output is a noisy steering mechanism. Evolution determines the quantity and placement of sensors. Noise in fitness tests discourages brittle strategies and leads to the evolution of robust, noise-tolerant controllers. Genetic Programming is used to model evolution, the controllers are represented as deterministic computer programs.

We present a novel evolutionary approach to robotic control of a real robot based on genetic programming (GP). Our approach uses genetic programming techniques that manipulate machine code to evolve control programs for robots. This variant of GP has several advantages over a conventional GP system, such as higher speed, lower memory requirements and better real time properties. Previous attempts to apply GP in robotics use simulations to evaluate control programs and have difficulties with learning tasks involving a real robot. We present an on-line control method that is evaluated in two different physical environments and applied to two tasks using the Khepera robot platform: obstacle avoidance and object following. The results show fast learning and good generalization.

We have evaluated the use of Genetic Programming to directly control a miniature robot. The goal of the GP-system was to evolve real-time obstacle avoiding behaviour from sensorial data. The evolved programs are used in a sense-think-act context. We employed a novel technique to enable real time learning with a real robot. The technique uses a probabilistic sampling of the environment where each individual is tested on a new real-time fitness case in a tournament selection procedure. The fitness has a pain and a pleasure part. The negative part of fitness, the pain, is simply the sum of the proximity sensor values. In order to keep the robot from standing still or gyrating, it has a pleasure componentton fitness. It gets pleasure from going straight and fast. The evolved algorithm shows robust performance even if the robot is lifted and placed in a completely different environment or if obstacles are moved around.

this paper, we argue that an effective way of doing this is through the integration of a rich collection of interacting techniques, organized in a principled, structured representation. These techniques include planners and parallel transition networks (Section 3.2) to aid in overall task control, and goal-based sensing, response, and (as necessary) physicsbased, kinematic or inverse kinematic behaviors to achieve environmentally-appropriate movements. Together they simplify: ffl the expression of local environmental influences without complicating their expression at the higher levels and ffl the expression of situational awareness and influences at a higher level without the added complexity of managing all potential lower level variability, while admitting: ffl the aggregation of the intentions and expectations associated with individual tasks; ffl the interaction of multiple agents, wherein agents can sense and react to the behavior and perceived intentions of other agents, as well as to the environment; ffl the distinction between expected alternatives in the way a situation may develop and seizure of opportunity or total surprise at circumstances; ffl the independent but coordinated control of a human agent's locomotion, upper body motions, visual attention, and communication systems (speech and facial expression); ffl the embedding of persistent data structures for cognitive knowledge or spatial maps as needed to remember or mark as known visible parts or characteristics of the environment;

A computer language is a very general form of representing and specifying an autonomous agent's behavior. The task of planning feasible actions could then simply be reduced to an instance of automatic programming. We have evaluated the use of an evolutionary technique for automatic programming called Genetic Programming (GP) to directly control a miniature robot. To our knowledge, this is the first attempt to control a real robot with a GP based learning method. Two schemes are presented. The objective of the GP system in our first approach is to evolve real-time obstacle avoiding behavior. This technique enables real-time learning with a real robot using genetic programming. It has, however, the drawback that the learning time is limited by the response dynamics of the environment. To overcome this problems we have devised a second method, learning from past experiences which are stored in memory. This new system allows a speed-up of the algorithm by a factor of more than 2000. Obstac...

Animating realistic human agents involves more than just creating movements that look "real." A principal characteristic of humans is their ability to plan and make decisions based on intentions and the local environmental context. "Animated agents" must therefore react to and deliberate about their environment and other agents. Our agent animation uses various low level behaviors, sense-control-action loops, high level planning, and parallel task networks. Several systems we developed will illustrate how these components contribute to the realism and efficacy of human agent animation.

One of the most general forms of representing and specifying behavior is by using a computer language. We have evaluated the use of the evolutionary technique of Genetic Programming (GP) to directly control a miniature robot. The goal of the GP-system was to evolve real-time obstacle avoiding behavior from sensorial. The evolved programs are used in a sense-think-act context. We employed a novel technique to enable real time learning with a real robot using genetic programming. To our knowledge, this is the first use of GP with a real robot. The method uses a probabilistic sampling of the environment where each individual is tested on a new real-time fitness case in a tournament selection procedure. The robots behavior is evolved without any knowledge of the task except for the feed-back from a fitness function. The fitness has a pain and a pleasure part. The negative part of fitness, the pain, is simply the sum of the proximity sensor values. In order to keep the robot fr...

We present a complete architecture for behavioral control of locomotion for both real and simulated agents and provide a design methodology for building the locomotion control systems that embody the architecture. A low-level locomotion engine controls an agent's actions directly based on intermediate-level reactive behaviors such as attraction and avoidance. High-level state machines schedule and control the reactive behaviors allowing for more "intelligent" decision processes, and an agent model provides a mechanism for varying locomotion according to agent state and personality attributes. In addition to providing specifications for a locomotion engine, we address the problem of selecting and organizing an appropriate set of behaviors. We present selection criteria and a method for partitioning the behaviors to aid in implementation. We discuss the challenges specific to human locomotion and explain how to...