Adaptive Applications of Intelligent Agents(2)

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

presents also a general architecture of intelligent adaptive agents and a set of key features that should be taken into consideration during the implementation of intelligent adaptive agents. These features include the goal and cause of adaptation, the relationship between adaptation process and the architecture of an agent or a society of agents, and others. Two examples of intelligent adaptive agents in the domains of air traveling and object identification are presented to illustrate different issues of the framework. In both examples, the agent was considered as a program agent learns from problem-solving cases how to adapt its model of other agents of the same group. Adapting the agent's model of other agents usually changes the course of actions the agent may follow in different situations. They demonstrated their approach using a distribute AI problem, called Predator-Prey. The problem is concerned with four agents (predators) attempting to capture another agent (prey). Each predator adapts its moves based on the potential moves of other predators to avoid conflicts. They proposed also solutions to avoid deadlock situations due to agents over-learning.

controls the input data, parameters, execution plan, set of cost functions, and a set of learning systems. Also, each agent has a set of heuristics to control the adaptation process. These heuristics were designed to minimize the number of learning systems used by the agent, to change the cost of attributes or attribute values to reach better solutions, etc. One of the learning systems used by both agents is the AQDT-2 system [7]. The AQDT-2 system learns task-oriented decision structures from decision rules.

2. Related Work

Since adaptation is desired whenever errors or unexpected changes in the environment occurred, it is very important to detect these errors or changes, determine their cause (if possible), determine the correct course of modifications, and adapt the agent functionality to resolve such situations. Laird, Pearson, and Huffman [6] introduced a very nice approach to model adaptation in agents. The approach characterizes the adaptation process into three levels of knowledge and control. These levels are the reflex level for reactive response, a deliberate level for goal-driven behavior, and a reflective layer for plan deliberation and problem decomposition. Their approach demonstrated adaptation at both the reflex and the deliberate levels. At the reflex level, the domain theory is modified and extended to determine needed actions for similar situations. At the deliberate level, the agent uses the reflective knowledge to update its course of action.

If the mind controls the body, the body is the main

information tributary to the mind. Haigh and Veloso [3] developed an approach for adapting the domain models of a planner based on a robot's direct observations of the environment. The approach introduced an agent, Rogue, which uses the planning and learning system Prodigy[12] to support the robot Xavier [11] in performing physical tasks. After performing each task, the robot Xavier provides the agent Rogue with its observations about the environment. Rogue responds to the observations by dynamically updating the domain model. This update may affect the set of tasks that the robot needs to perform. The robot then starts performing tasks of the modified plan. Haynes and Sen [4] introduced adaptation as a key component of any society of intelligent agents. Each In virtual reality simulation, agents can independently transfer through a network of computers to accomplish a task. Rus, Gray, and Kotz [10] introduced adaptive agents as systems that can completely terminate their existence at a given location of a network, transform to a better location to accomplish the given task, and resume the execution of that task. Such agents have capabilities to: 1) sense the state of network (e.g., to check if the local host is connected, to find out if a site is reachable, or to estimate the load of the network); 2) monitor conditions of software resource (e.g., monitor activities of a site or another agent which expected to receive or obtain relevant information to the current task); 3) interact with other agents (e.g., an agent may need to know other agent locations in the network, gather information about the tasks they can perform, or request a task from another agent).

In a multi-agent society, predicting environmental changes is another approach to plan for intelligent adaptation. To predict and adapt to environmental changes in information-based multi-agent systems, Decker, Sycara, and Williamson [2] presented an approach where a matchmaker information agent gathers organizational information about all agents' functionality, each agent plans the control-flow of its actions or decisions using information about the relationships between all current tasks, all agents utilize a flexible scheduling mechanism, and each agent can control its active execution load.

3. Examples of Adaptive Agents 3.1. Adaptive Travel Agents

This section presents an adaptive travel agent that optimizes a knowledge base for different user requests. The agent architecture follows the proposed general architecture described in section 3.2. The agent utilizes the AQDT-2 learning systems and a program for modifying the input files to the AQDT-2 system. The initial knowledge was learned by the AQ15c system [1]. For each new customer, the agent acquires information about the customer’s preferences (i.e., given situation), then it updates its cost functions, parameter settings, and other criteria. Updating these criteria biases the AQDT-2 system to optimize the initial knowledge for the given situation. Information about the given situation and the settings of all criteria is stored in the agent data

百度搜索“77cn”或“免费范文网”即可找到本站免费阅读全部范文。收藏本站方便下次阅读，免费范文网，提供经典小说教育文库Adaptive Applications of Intelligent Agents(2)在线全文阅读。