1. Bioagents and Biorobots David Kadleček, Michal Petrus, Pavel Nahodil
Dept. of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague Technická 2, Prague, Czech Republic
INF 90
Vegetative System Block - represents the homeostatic part. A set of drives and chemicals is defined here and used to measure events, for unsupervised learning. Stimuli coming from the Perception Layer are transformed to drives. These drives can accumulate, be transformed into electronic signals and stimulate or inhibit creation of another drive.
Action Selection Block - represents the reactive part. All tendencies of the system as well as stimuli from another blocks are combined here and “the best action” according to the situation is selected. The architecture enables the combination of reflexes, instincts and tendencies from higher cognition parts in one block and takes all of them into account.
Parallel Execution of Low-level Actions - agent can select and perform more than one action in a time, ie. one agent can drink, run and talk in one time
Pre-computed Rate between Consummatory Acts and Appetitive Behaviors – agent makes estimation of duration of an appetitive behavior based on statistical evaluation of the environment and his previous behavior
Conception Block - represents the deliberative part. Contains all higher cognition parts as planning, learning and social behavior and is responsible for conceptive thinking and planning. The Conception Block can contain sub-blocks specialized for various behavior.
Perception Layer - transforms low-level sensory data into higher-level description of the environment in terms of features and their positions
Actuation Layer - transformation from actions into a set of lower level motor commands
Attention Layer – Various types of a focus of attention
INTRODUCTION
This poster presents overview to a research of agents and robots encompassing designs inspired by principles of biological systems. The approach is based on the idea that less or more complex biological systems are constructed from a set of invariant patterns. Combination of these patterns shape resulting behavioural class of the biological system. After placing to an environment and inside a group of another biological systems, the system evolves to an instance of this class. The interaction with the environment and another biological systems sets up its free parameters and creates resulting behaviour. We call the entities bioagents or biorobots, because they embody basic patterns of biological systems. Resulting systems are able to act in a very complex and dynamic environment and can incorporate various levels of intelligence. They are able to act in very different domains with small changes. They evoke the seeming of life, but they are a form of artificial life only. The implementation scenario is such as that the systems are simulated in a simulation environment first and then deployed to a target domain, no matter if it is a walking robot or an e-Commerce agent. This deliberative, reactive and homeostatic pattern is the base for a biosystem and we have tested it in several applications, from physical mobile robots, through artificial life simulations.
FEATURES OF THE TESTED ARCHITECTURE
Emergent, proactive, autonomous and social hybrid architecture with various types of adaptation
Reflexes, instincts, vegetative system and higher cognition parts work together in one system
New action selection mechanism
Testing environment with sufficient diversity enables testing of very complex multi agent tasks and behavior
Scalable and flexible XML-Java implementation
EXPERIMENTS
The Experiment “Survival and Adaptation” - agents behave “cleverly” in order to survive in different environments, satisfy needs and adapt in several ways
The Experiment “Population Dynamics of the Predator-Prey System” - tests population dynamics in systems with high number of agents
The Experiment „Postman” - social hierarchies and cooperative solving of more complex tasks in a multi-agent system
ARCHITECTURE
Figure 2: Simulation environment
REFERENCES
ARKIN, R., C.: Behavior-Based Robotics: Intelligent robots and autonomous agents. The MIT Press, 1999.
HAYKIN S.: Neural Networks. A comprehensive foundation. Prentice Hall, 1999.
KADLEČEK, D., NAHODIL, P.: New hybrid architecture in artificial life simulation. Advances in Artificial Life. - Lecture Notes in Artificial Intelligence No 2159, Springer Verlag, Berlin, pp , 2001.
MCFARLEND, D. BOSSER, T.: Intelligent behaviour in Animals and Robots A Bradford Book, The MIT Press, 141–172, 1993.
Figure 1: High level architecture