New Hybrid Architecture in
Artificial Life Simulation
David Kadleček, Pavel Nahodil
Dept. of Cybernetics, Faculty of Electrical Engineering,
Czech Technical University in Prague
Technická 2, 166 27 Prague, Czech Republic
[email protected], nahodil@ lab.felk.cvut.cz
Vegetative System Block - set of drives and chemicals is defined and used to
INTRODUCTION
Intelligent life forms are partly bottom-up and partly top-down structures that have been
created by an evolutionary process. This hybrid agent architecture tries to find a
compromise between bottom-up and top-down approaches. A system based on this
architecture has properties that cannot evolve in time as well us parts that evolve and
adapt in time. The most important parts of the system are the Vegetative System Block,
the Action Selection Block and the Conception Block. The main aim is to show how to
connect several levels of the mind, namely reflexes and instincts, self-preservation and
deliberative thinking, into one system. The work shows that the resulting less complex as
well as more complex behaviors are influenced by all the levels of the mind. Such a
combination gives a powerful and robust system able to survive and evolve in various
environments. Agents based on this architecture are emergent, pro-active, social,
autonomous and adaptive. All structured information inside the agents and virtual
environments as well as the messages flowing between the agents are described with the
XML language. The proposed architecture, its possibilities, advantages and disadvantages
are tested in a virtual environment implemented in Java. The combination of the Java and
XML enables us to create a virtual environment that is very flexible and has high
diversity of properties and conditions that influence an agent’s behavior.
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 - 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 - 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
FEATURES
Actuation Layer - transformation from actions into a set of lower level motor
•Emergent, proactive, autonomous and social hybrid architecture with various types
of adaptation
•Reflexes, instincts, vegetative system and higher cognition parts work together in
one systém
•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
ARCHITECTURE
Conception
Vegetative System
Attention Layer – chooses objects on which is focused agent’s attention
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
(Scripts,Petri Nets,ANN...)
Petri Nets
o
o
o
Scripts
function refillVehicle()
{
step forward;
open tank;
pour oil;
close tank;
}
e tc...
Neural netw orks
(reflexes, basic actions...)
........
Perception Layer
Actuation Layer
XM
XM
L
Perception->Stimuli Layer
(drives, chem icals...)
Action Selection
........
Agent's core architecture
commands
L
Figure 2: Simulation environment
REFERENCES
Environment
Figure 1: High level architecture
1. Ferber, J.: Multi Agent Systems. An Introduction to Distributed Artificial Intelligence. Addison-Wesley.
(1999).
2. Svatoš, V.,Nahodil, P., Kadleček, D., Kurzveil, J., Maixner V.: Community of Mobile Robots in Education.
In: Proc. of IFAC EPAC 2000 Conference: The 21st Century Education in Automation and Control,
Skopje, Macedonia, (2000), 53 - 57.
3. Tyrrell, T.: Computational Mechanisms for Action Selection. Ph.D. Thesis, Edinburgh University. (1993)