Today, the costs of data redundancy have decreased to some extent. Being able to more efficiently store data in tables "eliminated much data storage and provided much more flexible data access" (Data redundancy, 2010, Logic). Controlled redundancy with careful limits on unauthorized access can eliminate the problem of data inconsistency and having one set of data altered but not the other. According to Junhu Wan's 2006 article "Binary equality implication constraints, normal forms and data redundancy," inconsistent "redundancies can be prevented if the instances of the two relation schemas do not contain overlapping information," and thus the benefits that can be accrued from redundancies may outweigh their detriments (Wan 2006, p.2).
References
Data redundancy. (2010). Computing students. Retrieved October 17, 2010 at http://www.computingstudents.com/dictionary/?word=Data%20Redundancy
Data redundancy. (2010). Logic data UK. Retrieved October 17, 2010 at http://www.logicdata.co.uk/data-security/Data-Redundancy/data-redundancy-dbms/
Wan, Junhu. (2006). Binary equality implication constraints, normal forms and data redundancy.
Retrieved October 17, 2010 at citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.114.3912.
Intelligent agents
According to Michael Luck's article on "Agent-based computing," intelligent agents are "defined to be autonomous, problem-solving computational entities capable of effective operation in dynamic and open environments" (Luck 2006, p.35). Intelligent agents are, much like human intelligent agents, capable of interacting with computerized and human agents in a dynamic fashion and are even able to take into consideration that the people and other systems with which they are interacting might have conflicting aims. Multi-agent systems are capable of understanding change, and are thus more appropriate for representing real world environments. Agent-based computing has been used in both biological simulations of the human body and also in military computer simulations. However, Luck characterizes...
Significantly, the financial industry has been the most eager user of agent-based systems because of its ability to create decision-making models which change in their outcomes, based upon a variety of different potential market circumstances (Luck 2006, p.36). However, all businesses that have adapted intelligent agency in their computer systems have benefited because of the assistance it is able to give in guiding resource allocation, depending on split-second, sudden changes in the environment.
To create such intelligent agents that are capable of reacting to change at a moment's notice, intelligent systems must be able to learn from other agents and act upon user preferences, find ways to negotiate and cooperate with other agents, and develop "appropriate means of forming and managing coalitions" between different entities (Luck 2006, p.35). Intelligence is not defined as mere reactivity, but a form of complex reactivity. For example, one cargo company that has deployed intelligent agency found that "the system can dynamically adapt plans in response to unexpected changes, such as transportation cost fluctuations or changes to vessels, ports or cargo. Agent-based optimization techniques not only provided improved responsiveness, but also reduced the human effort necessary to deal with the vast amounts of information required, thus reducing costly mistakes, and preserving the knowledge developed in the process of scheduling" (Luck 2006, p.35). Changes were not confined to one sphere -- intelligent agents must be truly forward-thinking in their ability to examine diverse influences, such as changes in input costs and time-related scheduling. They possess, because of their ability to systemize different areas of content, accurate, a flexibility in some instances even superior to that of a human.
Reference
Luck, Michael. (2006, May). Agent-Based Computing.GeoConnection International Magazine.
Retrieved October 17, 2010 at http://www.geoconnexion.com/uploads/agentbased_intv5i5.pdf
