Existing Agent Technology to Support Integrity
control incoming e-mails; learning via a neural network makes this agent a perfect example of what a self-adaptation verifier should do to achieve the goal of learning. There are many techniques developed in machine learning, such as data mining (Cho, Kyeong &
Hie, 2003) and neural networks, observation, human feedback, or learning from other entities (Hu & Wellman, 1998) that can be used to implement the self-adaptation verifier.
It is quite possible that an environmental verifier will also have learning capability.
Because the environment is essentially dynamic, it is impossible for an agent to consider and respond to every stimulus. A more desirable behavior is to focus on relevant information and be proactive in the search for information. Learning is necessary for an agent to determine what is relevant to the current context and what is not.
Because its task involves monitoring changes occurring in environment, there is a need for the environmental verifier be able to move through different environments and through different perspectives. Agent mobility is a popular area of agent research and predicted to be the future of the Internet (Kotz & Gray, 1999). The environmental verifier can send small programs (robots or crawlers) to retrieve new information from relevant locations in a distributed inquiring organization or from locations external to the organization (such as Internet search engine robots) (Menczer, 2003). Mobility is also valuable for the basis verifier that is in charge of maintaining global consistency across a decentralized knowledge base. There are arguments for the trade-off between the increased complexity of mobility and the benefits that mobility provides. These trade- offs must be considered before mobile agents are implemented.
Table 2. Summary of AI/Agent Techniques that Support theIntegrity Checking Feature of the Inquiring Organization
Agents of the Integrity Checking
Component Critical Requirements Techniques Basis Verifier Accuracy of system basis Truth maintenance Environmental
Verifier Knowledge store continually reviewed
for accuracy in changing environments Perception, learning, and mobility
Self-adaptation
Verifier New action requirements Default reasoning and learning
Analysis Integrity
Verifier Prevents assimilation in error; prevents other knowledge store components from being assimilated because of an error
Proof and explanation
Time/Space
Assessor Time/space assessment Temporal logic
Conclusion
In this chapter, we propose that agent technology can be used to support knowledge creation and management in an inquiring organization. Inquiring Systems Theory (Churchman, 1971) provides a basis for our conceptualization of an inquiring organiza- tion and its components; agent technology is used as the technological design frame- work. The concept of agent technology has been used to conceptualize and model agents and multiagent systems that serve to support the foundations of an inquiring organiza- tion and its characteristics.
Although we focus on the technological design aspect of inquiring organizations, we not only embrace Churchman’s admonitions to maintain multiple perspectives, flexibility, learning, and social orientation by designing agents to represent different tasks and perspectives but also strongly advocate and design for the human element in an inquiring organization.
Humans play an indispensable role in these systems. The most obvious is that the modeling and design processes require input from human experts. Individuals provide the domain knowledge to be captured and verify the correctness of its representation during system design, construction, and implementation. The elements and models inherent in the design and use of a system must be understandable by all stakeholders (Kilov, 2002) and therefore depend not only on the expertise of the designers but the expertise and direction of the users as well. For instance, the rules for filtering relevant data need to be defined by system users in their specific domain context. While agents may play a large role in producing relevant information, there will always be a need for the system user to decide whether a piece of information is relevant to a specific context and on the assessment of the degree of importance. Ultimately, of course, the function of choice (that is, the decision) resides with the organizational member. The system described here is a support system; the object of that support is the system user. Thus, system users are not diminished by the system but rather empowered by it. The system described here will not simply automate but will informate (Zuboff, 1985).
The system conceptualized here has the ability to facilitate all components of a knowledge management system: knowledge creation, knowledge storage and retrieval, knowledge transfer, and knowledge application (Alavi & Leidner, 2001). These concepts are important to any organization desiring to exploit its knowledge resources but are particularly suited to inquiry. We believe that this support system framework will lead to a sustainable materialization of and support for the emerging form of an inquiring organization.
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