Adaptive Applications of Intelligent Agents(4)

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

TWA, or the first flight is not an overnight flight (e.g., short flight or one starts early in the morning).

Another example of an adaptive task is when the user requires an overnight trip. In this case, the agent assigns lower costs for all overnight flights. The agent runs AQDT-2 with the new task. AQDT-2 learns the decision tree shown in Figure 4-b. Parts of this decision tree show classifications of all trips which include an overnight flight. All such trips consist of three flights. The same Figure shows also that there are four preferred ways for a trip with an overnight flight, two of which have two a set of decisions or actions. Imam and Gutta [5] proposed an approach to improve the recognition of visual objects using adaptive methodology.

overnight flights.

Figure 4: Examples of adaptive situations.

A more complicated scenario would be, For example, if the customer asks for United airlines in as many flights as possible. The agent defines lower costs for all reduces the cost of the value 3 of attribute xi7). The agent groups all values representing other airline companies into one value “~”. The agent runs the AQDT-2 system with this new task to obtain the decision tree in Figure 5. The agent determined two preferences. The first has three trips and the third trip is a United flight. The second has four flights and requires staying over night. Note that the third preferred node may mean that there are other trips where the first or the second flight are served by United airlines. To obtain more details about these trips, the agent is supposed to set the cost of both attributes x37, x47 slightly higher and runs the AQDT-2 system to obtain a decision tree for the new task.

3.2. Identification Agents

The second adaptive agent presented here is an object identification agent. Object identification agents can widely be used by federal investigators to identify finger prints or facial images, companies to recognize identification cards and employees codes, by medical labs to diagnose diseases, by robots to identify known objects or surrounding environment, etc. Object identification is a part of the agent functionality. Usually, the results from the recognition process are used to form

United flights.

To illustrate the architecture of the agent, assume that the agent has a library of classifiers to recognize a set of objects. Each classifier can recognize the objects based on different set of characteristics. One of the agent’s goals is to determining which classifier should be chosen to obtain accurate recognition of a given object. The agent is not allowed to acquire any information about the testing object, however, it can use any number of classifiers to recognize that object. From a set of examples of recognition, the agent learns an optimized plan for recognizing any given object. The adaptation is done during the process of generating an optimized plan. Figure 6 shows an algorithm for identification agent.

Input: A set of training examples (records of recognition)

described by a set of attributes A.

Output: A tree shows a set of optimized plans of classifiers

needed for object recognition. : Quantize all attribute values in the records of

recognition.

: Specify the learning task for the agent (in this case, the

decision structure should have minimum number of classifiers and minimum number of levels). (*) For each attribute in the set A (i.e., classifier),

repeat steps 3 and 4. : Set the cost for that attribute lower than all other

attributes in the set A, and use AQDT-2 to learn decision tree. : Save the attribute name, the number of nodes, and

number of levels. Assign similar cost to all attributes in set A.

: The attribute produced the minimum number of nodes

and minimum number of levels, say C, is permanently assigned the lowest possible cost among the attributes in set A. Remove C from A. For each branch stemming from the node C, repeat

steps 6 to 8.

: Assign a copy of the attribute set A to each branch.

: If the node connected to the branch is a leaf node, skip step 8.

: If the branch is connected to a non-leaf node, go to (*).

Figure 6: The adaptive algorithm used by the agent.

The agent uses a set of heuristics to control the adaptation process. For example, the agent should recognize all objects using the minimum number of

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