Adaptive Applications of Intelligent Agents(3)

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

structure. After learning the optimized structure, the agent tests the knowledge against a set of constraints and heuristics. The goals of the agent are to: 1) determine accurate knowledge, 2) minimize the number of nodes in the obtained structure, and 3) minimize the average number of levels in the structure. Figure 1 shows a simple architecture of the main components of the travel agent. Also, Figure 2 shows a general algorithmic also sell tickets for any flight starts from the first destination to any intermediate destinations. The agent discovered that the customer’s preferences can be summarized by 8 attributes, Table 1. All possible trips from Washington to Tokyo were split into two categories. The first category of trips labeled (many customers requested these routes), while the rest of the data was labeled description of the adaptation process.

Task-orientedFigure 1: Architecture of the Adaptive Travel Agent.

The data was generated using Mugglton’s program[8]. User’s preferences of a flight are represented using a set of attributes describing: 1) if the customer requires over night stay, 2) the average waiting time during the whole trip, 3) if it is an over night flight, 4) if its frequent flight program is compatible with the customer’s program, 5) the kind of entertainment the airline company provides, 6) if smoking is allowed, 7) the name of the airline company, and 8) the number of meals served during the trip. Table 1 describes these attributes and their possible values.

Input: A set of decision rules or examples and description of

one or more tasks.

Output: A situation-oriented decision structure/tree for each

decision-making situation.

: For each decision making situation, the agent repeats

steps 2 to 4.

criteria) and uses the cost functions to update the ranking of the attributes.

structure/tree. It selects the top ranked attribute, assigns it to a node, creates branches equal to the number of its values, and divides the decision rules associated with this node into subsets each corresponds to one branch. inexpensive attributes can be used for further classifications of the decision rules at a certain node, the agent generates a leaf node with all possible decisions at this node. The agent can also determine a confidence probability for each decision or solution.

Figure 2: Description of the adaptive algorithm.

The travel database is concerned with trips between Washington D.C. and Tokyo. Each trip consists of two or more flights. The agent deals with six airline companies. Non of the six companies offers a direct-flight trip between the two destinations. The agent can agent asks the customer a set of questions to determine the most appropriate flight. In this paper, the agent will attempt to discover interesting combinations that of interest to the customer in order to optimize the process of finding a preferred trip for the customer.

Table 1: The set of attributes used in the experiments

(“i” : stands for the flight number).

Attribute Values

1

2 3

4

5

xi1 over-night-stayyes no xi2 waiting-time x< 11<x<3 3<x<6 6<x>10x>10

xi3 over-night-flightyes no xi4 frequent-flight yes no xi5 entertainment all music movie games none xi6 smoking no allowed xi7 airline-companyTWACAL PanAM United KLMJAL xi8 No.-of-meals one two more none

The agent first learns a classification of preferred and non-preferred trips. The agent uses the descriptions of the preferred trips to assist the user in selecting the best trip. Figure 3 shows a decision tree, obtained by AQDT-2, that classifying preferred and non-preferred trips. From this decision tree, one can observe that preferred trips depend highly on the airline company, and the frequent-flight-mileage. The agent offers any new customer the preferred trips. If the customer requires other options, then the agent presents him/her the non-preferred trips.

Figure 3: A decision structure obtained by AQDT-2 in

its default setting.

Adaptation is required whenever the user has a specific request that can not be answered directly by the agent. For example, suppose that the customer was interested in the frequent flight mileage program. For this task, the agent assigns lower costs to all attributes representing the frequent flight mileage in all flights, and runs the AQDT-2 system. Figure 4-a shows a decision tree obtained by AQDT-2 for the given task. The agent’s best choice is to propose the customer a trip consisting of three flights, where either the third flight should be on

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