1Brooks, R' A': Intelligence without Representation' In: Artificial Intelligence, Vol' 47, pp'139160

1
INTRODUCTION
SIGNIFICANCE
BEHAVIOURAL INFLUENCE
The behaviour attributes makes another quality in
the temporal evaluation of any instant situation.
We talk about internal motivations that play
excitating and suppresive role at once. The
internal rules stimulate partial sub-goals to its
satisfying.
The experiments were evidenced a relevance of the
behaviour-based method (space cover task) and
gradient one (phototaxis) in the robot control.
The methods use environment as actual
representant of the world without internal map.
Therefore, the application depends on the initial
arrangenment of the workspace namely.
Many tasks in the robot control schedule some
navigation in unarranged environment. Their
intelligence is independent on sophisticated
understanding. The reactive control demonstrates
such solution of the problem in the bottom-up
way. We deal in the taxe movement case. Our
approach also utilise behavioural features of
control system (see Fig.1). These ones are
inspirated by insect seeking behaviour using
gradient attributes of luminescence, pheromone,
or another sign substance.
The internal motivations are integrated from
history of actual actions and internal sub-goals
(system experience) including explicit
motivations (system setup). The actions are
encoded to the same basal ways as the sensor
representants in this concept.
The advanced behavioural synthesis also shows an
expert capability of the relevance weighting by
its possibility.
Corresponding homogenous areas cover a map of the
basic actions (see Fig. 3). The rating is similar
to fuzzy grade of desiarability of action.
The distribution of fields in the map of the
basic actions depends on the practice or an
improved adaptation. We prefere a reinforcement
learning to any activity cluster.
The gradient prereqisity was approved in our
experiments as well. The behavioural trends does
not performed in the brief experiments yet.
SUITABLE METHODS
RELATED METHODS
SELECTION MECHANISM
Singular inputs and internal stimuli are competed
in the following order flow
Our alternative decision omits a knowledge
synthesis based on the geometric interpretation
of the workspace. We prefer an instant perception
of the local neighbourhood 1. Such processing
is based on a functional decomposition that
reflect a specific occasion.
The proposed approach could be itemized in the
comparation with above-considered methods as
follows

This approach utilizes following domains



The reactive rule was yet encoded by the
reactictive representation to potential field.
Now, we select the most preferred action throug
assignment of extreme in the field.

The probabilistic estimation works in the
probabilistic field, which is construed at the
same level as the potential field.

REFERENCES
REACTIVE REPRESENTATION
EXPERIMENTS
Proposed system architecture was tested on the
mobile robot platform3. There were achieved
experiments focused on gradient-based navigation
in unknown enviroment. Implemented robots
behaviour embodies reactive character. The
executed actions was educed from local
environmental information without design of
internal representation.
1 Brooks, R. A. Intelligence without
Representation. In Artificial Intelligence, Vol.
47, pp.139-160. MIT Press, 1991 2 Starý, J.
Rozírený HW pro mobilního robota. Diploma work,
CTU Prague 2000. In czech. 3 Kurzveil, J -
Maixner, V - Svato, V. Autonomous Mobile Robot
Platform. In Proceeding of 3th International
Student Conference on Electrical Engineering
"Poster 1999", p. IC 26, Prague, May
1999. 4 Khatib, O. Real-time Obstacle
Avoidance for Manipulators and Mobile Robots. The
International Journal of Robotic Research, Vol.5
No.1, pp.90-98, 1986. 5 Balch, T. Behavioral
Diversity in learning Robot Teams. Doctoral
thesis, Georgia Institute of Technology, December
1998. 6 Large, E. W. - Christensen, H. I. -
Bajcsy, R. Scaling the Dynamic Approach to Path
Planning and Control Competition among
Behavioural Constraints. The International
Journal of Robotic Research, Vol.18 No.1,
pp.37-58, 1999.