The increase of the processing power of personal computers in the last decade resulted in a notable progress of the artificial animal (animat) construction and simulation field. Regardless of the achieved results the coding of an animat’s behavior can, to someone unfamiliar with physics of motion and robotics, seem like pure witchcraft. Not to mention the wealth of ethological knowledge required regarding the behavior of the animal that is being modeled. In this article we suggest the use of fuzzy logic as the basis of an animat’s decision about its next step. We hypothesize that by using linguistic programming based on common sense unclear and even partially contradictory knowledge of the animal’s behavior, we can achieve comparable if not even better simulation results than with the classical crisp implementation. The following article presents an investigation of our theories on the case of a boid – a special type of animat – limiting itself on the boid’s urge of alignment with its flockmates.
[1]
Craig W. Reynolds.
Steering Behaviors For Autonomous Characters
,
1999
.
[2]
Stewart W. Wilson.
Knowledge Growth in an Artificial Animal
,
1985,
ICGA.
[3]
Craig W. Reynolds.
Flocks, herds, and schools: a distributed behavioral model
,
1987,
SIGGRAPH.
[4]
Miha Mraz,et al.
Intelligent bush fire spread prediction using fuzzy cellular automata
,
1999,
J. Intell. Fuzzy Syst..
[5]
Jernej Virant.
Design Considerations of Time in Fuzzy Systems
,
1999
.
[6]
Graham Kendall,et al.
Evolving Collective Behavior in an Artificial Ecology
,
2001,
Artificial Life.
[7]
Xiaoyuan Tu,et al.
Artificial Animals for Computer Animation: Biomechanics, Locomotion, Perception, and Behavior
,
1999,
Lecture Notes in Computer Science.
[8]
Miha Mraz,et al.
Notes on fuzzy cellular automata
,
2000
.