Adaptive Applications of Intelligent Agents(5)

The paper presents two applications of intelligent agents to support the concept of adaptation defined in previous work. The concept of adaptation differs from the concept of intelligence and they do not necessarily associated with each other. Agent adapta

classifiers. The agent uses the AQDT-2 learning system to create a complete plan for recognizing new objects. Two actions can be taken at each step of the plan, either to the object or to plan describes a sequence of actions based on the maximum value given by the classifier to a decision class. Whenever the action is “recognize”, the agent assigns the decision class with the maximum value to the tested object. The agent adapts the cost functions and the different parameters to obtain the optimal plan. Figure 6 shows a brief description of the adaptive methodology of the agent. The algorithm stops after testing all intermediate nodes. This algorithm is not concerned with building the decision tree. However, it controls the process of learning decision trees.

Select aClassifierThe application presented here was applied on a domain of hand gestures. A total of 150 gestures representing 15 different gestures were obtained. Three different testing combinations, The AQDT-2 program was modified to allow all nodes to contain actions in addition to the tested attributes. The agent generates a table of actions with their possible places. For example, in this case there are two actions “Select” and “Recognize”. The “Select” action is assigned to any internal node. The “Recognize” action is assigned to leaf nodes only. The goal of the adaptive process is to maximize the recognition rate of objects using the minimum number of classifiers. Figure 7 shows a portion of the complete set of plans obtained by the agent.

<=0.35Recognize

>0.35 &<=0.45Select aClassifier>0.45 &<=0.55Select aClassifier>0.55 &<=0.65Select aClassifier #4

>0.65 &<=0.75Select aClassifierClassifier #3

>0.75 &<=0.85Select aClassifierClassifier #7

>0.85Recognize

Classifier #7Classifier #5

>0.25 &>0.85<=0.65

Recognize:

:

:

:>0.35 &>0.65 &>0.75 &

<=0.65<=0.75<=0.85RecognizeRecognizeRecognize

>0.35 &>0.45 &

<=0.45<=0.55RecognizeGest. #11Gest. #1Gest. #4Gest. #3Gest. #8Gest. #5Gest. #2Gest. #10

Figure 7: Portion of a complete plan for recognizing visual objects.

adaptive behavior which usually driven by changes in the 4. Summary

environment; 3) Complete Adaptation—where the The paper proposed a framework for developing

internal systems of the agent are adaptive and its external intelligent adaptive agents. In this framework, intelligent

actions reflect adaptive behavior. adaptive agents are defined as systems or machines that

utilize inferential or complex computational The paper presents a set of fundamental issues in the

development of any intelligent adaptive agent including methodologies to modify or change control parameters,

knowledge-bases, task plans, problem-solving modeling the cause and the goal of the adaptation

process, the relationship between the adaptation process methodologies, course of actions (for the same agent or

and the agent architecture, and other criteria distinguish for other agents), or other objects in order to successfully

adaptation in multi-agent society from adaptation in accomplish a set of tasks that are of interest to the user.

single agent. Also, the framework presents intelligent The framework distinguishes between two types of

adaptation as a process that indicates the agent’s ability intelligent adaptation, external (behavior) and internal

to accomplish: different tasks within the scope of the (systematic). Intelligent adaptive agents are classified

agent functionality, similar tasks within different into three categories based on the agent capabilities on

environments, similar tasks within similar environment performing external and internal adaptation. These

but using different problem-solving methodology, etc. categories are: 1) Internal Adaptation—where the

The paper presented two adaptive agents that control internal systems of the agent are adaptive, but its

adaptive (as well as non-adaptive) systems. The results of external actions do not reflect any adaptive behavior; 2)

utilizing these systems are used in accomplishing External Adaptation—where the internal systems of the

external actions. These actions reflect adaptive behavior.

agent are not adaptive, but its external actions reflect

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