List of Figures
1.1
Emergence in complex systems. cf. [116]. . . . . . . . . . . .
2
1.2
Examples of complex adaptive systems, their models and common characteristics. cf. [171]. . . . . . . . . . . . . . . . . .
4
1.3
Bifurcation diagram in a logistic map. Adapted from [122]. .
5
1.4
Examples of Karl Sims’s creatures. cf. [180]. . . . . . . . . .
8
1.5
Modeling process in biology simulations. cf. [107]. . . . . . .
12
1.6
Various time steps showing ant colonies finding and forming
routes to food sources. cf. [23]. . . . . . . . . . . . . . . . . .
13
1.7
A system working in the environment. The system is composed of three elements working together to make the system
work efficiently. Output produces a feedback, that produces
change in the system as time progresses. . . . . . . . . . . .
14
1.8
Research areas of ‘Scientific Computing’ and ‘Distributed
Computing’ have a close relationship in agent-based modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
15
2.1
Scientific method. cf. [61]. . . . . . . . . . . . . . . . . . . .
18
2.2
Separate researches in AI. . . . . . . . . . . . . . . . . . . .
19
2.3
State and X-machine diagrams. . . . . . . . . . . . . . . . .
20
2.4
Your mind designed for CogAff Project. cf. [183]. . . . . . .
24
2.5
Weak and strong notions of agent actions. Cf. [205]. . . . . .
25
2.6
Program represented as a tree and a string. cf. [50]. . . . . .
28
2.7
An agent can represent a single strategy or multi-strategies.
31
2.8
Evolvability of programs. . . . . . . . . . . . . . . . . . . . .
32
2.9
Nested hierarchy of swarms. . . . . . . . . . . . . . . . . . .
34
3.1
Block diagram of FLAME. cf. [76]. . . . . . . . . . . . . . .
44
3.2
State and X-machine diagrams of an ant foraging for food. cf.
[104]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
46
3.3
FLAME uses strict X-machine architecture - Memory, Functions, States and Messages. . . . . . . . . . . . . . . . . . .
48
3.4
Incorporating agile methodology in agent models. Modified
from [20]. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
49
3.5
Agile agent development process. . . . . . . . . . . . . . . .
50
3.6
Structure of basic agent. Agents represent any individual such
as a household, an ant or a firm. . . . . . . . . . . . . . . .
52
xix
1.1
Emergence in complex systems. cf. [116]. . . . . . . . . . . .
2
1.2
Examples of complex adaptive systems, their models and common characteristics. cf. [171]. . . . . . . . . . . . . . . . . .
4
1.3
Bifurcation diagram in a logistic map. Adapted from [122]. .
5
1.4
Examples of Karl Sims’s creatures. cf. [180]. . . . . . . . . .
8
1.5
Modeling process in biology simulations. cf. [107]. . . . . . .
12
1.6
Various time steps showing ant colonies finding and forming
routes to food sources. cf. [23]. . . . . . . . . . . . . . . . . .
13
1.7
A system working in the environment. The system is composed of three elements working together to make the system
work efficiently. Output produces a feedback, that produces
change in the system as time progresses. . . . . . . . . . . .
14
1.8
Research areas of ‘Scientific Computing’ and ‘Distributed
Computing’ have a close relationship in agent-based modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
15
2.1
Scientific method. cf. [61]. . . . . . . . . . . . . . . . . . . .
18
2.2
Separate researches in AI. . . . . . . . . . . . . . . . . . . .
19
2.3
State and X-machine diagrams. . . . . . . . . . . . . . . . .
20
2.4
Your mind designed for CogAff Project. cf. [183]. . . . . . .
24
2.5
Weak and strong notions of agent actions. Cf. [205]. . . . . .
25
2.6
Program represented as a tree and a string. cf. [50]. . . . . .
28
2.7
An agent can represent a single strategy or multi-strategies.
31
2.8
Evolvability of programs. . . . . . . . . . . . . . . . . . . . .
32
2.9
Nested hierarchy of swarms. . . . . . . . . . . . . . . . . . .
34
3.1
Block diagram of FLAME. cf. [76]. . . . . . . . . . . . . . .
44
3.2
State and X-machine diagrams of an ant foraging for food. cf.
[104]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
46
3.3
FLAME uses strict X-machine architecture - Memory, Functions, States and Messages. . . . . . . . . . . . . . . . . . .
48
3.4
Incorporating agile methodology in agent models. Modified
from [20]. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
49
3.5
Agile agent development process. . . . . . . . . . . . . . . .
50
3.6
Structure of basic agent. Agents represent any individual such
as a household, an ant or a firm. . . . . . . . . . . . . . . .
52
xix