customers flounce in the shopping mall freely
again.
• If customers in shop A have a coupon, they leave
the shop and do away with their coupon and they
flounce in the shopping mall freely again. In other
cases, customers buy goods.
• When customers finish buying goods, they leave
the shopping mall
• The shops count the number of their customers
Figure 6: Outline of shopping mall.
5.2 Generation of Model by Examinees
5.2.1 Precondition of Experiment
First, examinees receive an explanation about the
shopping mall model that is described in section 5.1.
Secondly, they generate the shopping mall model by
utilizing our event description method. Finally, we
compare required time, which examinees generate a
model by utilizing our approach and by writing raw
program codes for “artisoc”.
Two examinees have programming experience,
however they inexperienced in MAS or “artisoc”. So,
they learn how to use “artisoc” by reading its manual.
5.2.2 Result of Experiment
Table 1 shows the result of experiment.
Table 1: Result of experiment.
In experimental result, it is clarified that time,
which is required to generate a simulation model by
utilizing “artisoc”, is reduced by utilizing our event
description method. Additionally, our event descrip-
tion method is easy to use for not only person of ex-
perience but also amateur programmers of MAS.
However, simulation sometimes does not go well,
because users draw the model in wrong manners. For
example, in case of “leave shop event”, users have
to describe processing to reduce the number of cus-
tomers. However, because of a perceived notion that
the number of customers reduces naturally, users fail
to describe this procedure explicitly. So, this simula-
tion does not go well. We need to consider a method
to bridge the gaps between users’ perceived notions
and codes.
6 CONCLUSIONS
In this paper, we proposed a description method for
multi-agent simulation model utilizing typical action
patterns of agents in a programming environment that
defines the notation of simulation model based on
graph representation.
In this paper, we executed experiment and com-
pared required time, which examinees generate a
model by utilizing our approach and by “artisoc”. As
a result, it is clarified that time is reduced by utilizing
our event description method. Additionally, an event
description method that we propose is easy to use for
not only person of experience but also amateur pro-
grammers of MAS.
REFERENCES
Axelrod, R. (1995). The convergence and stability of cul-
tures : Localconvergence and global plarization. Tech-
nical report, Working Paper 95-03-028,Santa Fe Insti-
tute.
Hatakeyama, G., Kimura, K., Akiyoshi, M., and Komoda,
N. (2007). A programming environment for multi-
agent simulation based on graph representation. In
Proc. of 2007 Summer Computer Simulation Confer-
ence (SCSC 2007), in CD-Rom.
kke. Mas community. http://mas.kke.co.jp/index.php.
MacNealy, M. S. (1999). Strategies for Empirical Research
in Writing. Allyn Bacon.
Minar, N., Burkhart, R., Langton, C., and Askenazi, M.
(1996). The convergence and stability of cultures.
Technical report, Working Paper 96-06-042,Santa Fe
Institute.
North, M., Howe, T., Collier, N., and Vos, R. (2005). The
repast simphony runtime system. In Proc. of Agent
2005 Conference on Generative Social Processes.
A DESCRIPTION METHOD FOR MULTI-AGENT SIMULATION MODEL UTILIZING TYPICAL ACTION
PATTERNS OF AGENTS
319