Today Wolf gave a presentation on the ICEC '08 paper "A Semantic Web Architecture for Advocate Agents to Determine Preferences and Facilitate Decision Making". The paper deals with the architecture of a new kind of autonomous agents.
The main motivation of applying these personalized agents is that they can complement the cognitive limitations of the human mind, and therefore facilitate the decision making process to, reduce information overload (bounded rationality), increase work efficiency (i.e. speed up real-time managerial decisions), increase productivity (cost savings and ROI), increase solution (product or service) quality. Besides these tangible benefits, there are also intangible benefits, e.g. greater customer and employee satisfaction. In order to do this, these agents need to work effectively and efficiently with the human user. Meaning that the agent must learn the human user's interests, habits and preferences (as well as those of their communities). In an online retail example, recommendations can be given as to what to buy (product-brokering) and from whom to buy (merchant-brokering), based on customer criteria.
Agents and the human work in a bi-directional way through the interface called: Economic Dashboard.
"You cannot manage what you do not measure"
"What gets watched, gets done."
These statement demonstrate what the Economic Dashboard is, an "Organizational Magnifying Glass" – to focus the work of employees so everyone is going in the same direction! It business people: (1) Monitor, (2) Analyze, (3) Manage, (4) and Communicate and give feedback to the agent.
In order to work with the Economic Dashboard at all of the different organizational levels, these Economic dashboard has three types that relate to Business Intelligence:
Strategic BI: Achieve long-term organizational goals
Tactical BI: Conduct short-term analysis to achieve strategic goals
Operational BI: Provide a decision-making environment that reduces the latency between the time a significant business event happens and the business' ability to react to it.
In order to bring these personalized results, and work with the personalized results in the Economic Dashboard preferences are elicitated. Preference elicitations is the central concept of decision making and is fundamental for the analysis of human choice behavior, since people have different preferences for different roles. There are four methods or preference elicitation: (1) Questionnaire, which define roles, areas, objectives, and tasks; (2) Implicit feedback through user observation through browser extension (Piggy Bank, etc.), (3) Explicit user feedback through economic dashboard, and none intrusive sidebar in browser window, and (4) Business and Social Networks (Professional (intra company e.g. IBM, Linkedin, Plaxo, etc.) Personal (Facebook, Hi5, Hyves, etc.).
These preferences are saved in RDF stores, which allows the best abilities to apply Semantic Web agents.
In conclusion, this paper demonstrates the feasibility of Advocate Agents by presenting an architecture that integrates current technologies, such as Enterprise Service bus, XML, RDF, and machine learning techniques into a unique system and demonstrating that all the components of Advocate Agents can be built from already existing methods and elements.
After the presentation a discussion was held.