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4.5. LEARNING

To be truly successful, autonomous agents must adapt to their changing environments. A number of different techniques are being incorporated into autonomous agent systems to allow improvements to behavior.

In multi-agent systems, various agents can communicate with each other. An agent may learn simply by interrogating another agent for information that it has gathered. This new information and meta-information, such as the availability and reliability of the information received, is one example of agent learning.

Machine Learning techniques can also be used to help filter and find information. Agents are able to learn from the experience, using Machine Learning methods. For instance, softbot agents can analyze past user's queries to understand where and how interesting information can be retrieved. Inductive Logic Programming can be used to learn rules able to help the agents during the searching phase.

The use of Genetic Algorithms to aid in the development of better Information Filtering techniques is another approach explored by the MIT Media Lab. The information filtering agent is modeled as a population of profiles. Each profile searches for documents that match itself and recommends them to the user. User feedback can change the fitness of the profiles. If the user provides positive (negative) feedback for a document, the fitness of the profile that retrieved that document is increased (decreased). In addition, the profile can be modified in response to user feedback. The population as a whole continually adapts to the dynamic needs of the user.

5. FUTURE TRENDS AND SUMMARY

The use of intelligent agents is growing very rapidly. As this occurs, there are several issues and several technical areas that are becoming of increasing interest.

Among the issues that will become increasingly important as the use of intelligent agents becomes more widespread are the issues of privacy and security of derived agent information, and the impartiality of the agent system. An intelligent agent is given and infers information about the likes and preferences of a user in professional and personal areas. This information can be of great interest to marketers, business competitors, personal acquaintances, and many others. Many users will not want this information to be available to such people, and intelligent agent systems must insure that promised privacy is maintained.

In addition, there is the possibility of an agent being "influenced" without the user's knowledge -- either intentionally by the agent provider or by external means through a security breach -- to weigh decisions in a certain directions, e.g., to favor a particular brand or get information from a particular source. Users must be able to trust that the agent represents them with no bias put in by the agent provider, and that the agent is secure from subversion by external sources.

For truly flexible behavior, an agent application requires a very large common-sense knowledge base. Over the next several years we will see increasing emphasis on technology to build very large knowledge bases and to maintain them automatically. Many technical hurdles will have to be overcome, including automatic resolution of inconsistencies in knowledge from diverse sources, knowledge acquisition from text, and automatic refinement of knowledge bases with problem-solving experience.

Much progress is required on improving the interaction between agents and users. Agent interfaces will have to be multimodal, including speech, image, and language understanding capabilities, in order to have flexible dialogues with people (Reddy, 1996).

The construction of intelligent agent software should become less complex as intelligent agent object-oriented frameworks and applets appear. The availability of frameworks should also encourage developers to incorporate agent communication capabilities into their intelligent agents. As agent communications become more of an issue, the activity in this area should grow, with real-world development shaking out the most appropriate and robust agent communication mechanisms. The widespread availability of de facto standards such as Open Database Connectivity (ODBC) should greatly simplify linking intelligent agents to legacy data sources. New standards such as CORBA should also provide nice infrastructure bases to facilitate intelligent agent construction.

As the uses and benefits of intelligent agents grow, the agent development community should be careful to not oversell the technology and engender unrealistic expectations. It is incumbent upon technical practitioners to insure that they, their management, and related agencies realistically describe the potential of the technology, and refrain from promoting the technology as the new software panacea. Also, (adapting Marvin Minsky's advice to the AI community), it is important for agent practitioners not to so focus on a certain agent technologies that they neglect the wealth of available techniques. The best systems of the future will likely be the ones that are hybrids of many powerful agent and non-agent techniques.


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