Sborník 2009 díl 2. - Fakulta informatiky a managementu - Univerzita ...

Hana Mohelská, Petr Tučník MULTICRITERIAL DECISION-MAKING CAN DRIVE STRATEGY
The use of the MDM (as a control mechanism of an agent) brings the whole new
perspective to the decision making problem. While any human operator is limited by the
psychological capabilities when making decision (i.e. is able to take only a limited
number of variables into account), the agent has no such drawbacks. It is possible to
monitor dozens of environment attributes in one moment. However, there are several
conditions of such application:
1. The agent is able to monitor all the attributes which are needed to make
a qualified decision.
2. The agent is able to perceive an impact of its actions in the environment.
3. The environment is represented numerically or is it possible to transform its
description into numbers (representation problem).
The major issue usually lies in the third condition. The necessity of numerical
representation is given by the mathematical principle of the control mechanism [3]. Let
us remind the reader of the fact that we are discussing both software and hardware
agents at this point. The representation problem is significant especially for the
hardware agent case. Software agents already have an artificial environment which may
be easily adopted for the needs of MDM control mechanism.
When the environment is described in a numbers, each possible influence or its describable
aspect is represented as an attribute. The attribute is a quality of the environment, which may (in
general) occur in a certain degree of intensity. E.g. “distance to the target” or “temperature”. It
is usually easy to represent physical attributes, but it is also possible to represent internal of
mental states of the agent. For this purpose, the fuzzy sets representation may be used to capture
the non-numerical information. However, this may present a certain level of inaccuracy
to the decision making process.
The decision making process is typically made in cycles. The agent perceives its
environment by sensors and, when decision making situation occurs, the decision is
made. The next step is realization of action by agent´s actuators and the agent continues
to monitor its environment to perceive the impact of the change. The cycle returns to the
first step. If the MDM is used for decision support only, the decision making process is
concluded by human decision. In this case, the agent presents possible solutions and
function as an expert system.
The MDM control mechanism was adopted for the robot soccer agents/robots in the past
[9]. This is an example of a quick evolving environment with many possible solutions to
every configuration of the game. When a quick evolving environment is presented (with
a problems to be decided continuously) to a person, it is very difficult to handle this task
without the help of an automatic system.
The MDM may be used for decisions in many areas – logistics, planning, reactive
behavior, futures or stock market trading, etc. Usually, when there is a problem present
with many possible solutions and many attributes which must be considered, the MDM
offers an excellent, elegant way to handle this problem.
The decision making algorithm is fast and may be adapted to many situations. Although
the MDM can be used to handle tasks such as path selection problem or actuator
coordination, best results are obtained when applied to strategic decision making. Other
approaches are typically more efficient to handle lower level problems (movement,
coordination, direct control in general), but MDM is very suitable for highest level
decision problems. This may be applied to managerial (company) cases as well, as
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