Meeting Minutes – 26 November 2008

Today, Paul R. Schrater a guest researcher from University Minnesota, has made a presentation about active preference learning topics. He explained a topic of probability model in which an agent can derive a continous valuation formula which is a result from learning algorithm from a set of discrete data. He tried to explained the algorithm which can decide what approximate formula can be presented to an individual in order to find the projected result that they value highly in as few trial as possible, without making an accurate model the entire valuation surface.



About the presenter,

Paul R. Schrater hold a joint faculty position at the University of Minnesota, in the departments of Psychology and Computer Science. His current research interests generally involve using probabilistic methods to study issues in perception and motor control. He received his Ph.D. from the Department of Neuroscience of the University of Pennsylvania, under David Knill, then of the Department of Psychology and Eero Simoncelli in the GRASP Laboratory who at that time had a primary appointment in the Department of Computer Science at Penn. His dissertation involved a psychophysical and ideal observer analysis of local motion processing.

Meeting Minutes - 19 November 2008

Today, the LARGE session has had a very nice presentation from Carsten Block, a ready to finish doctoral candidate of Information & Market Engineering department, TU Karlsruhe Germany. At our session he presented the topic of "Market-Based Control and Agent-Based Trading in Combined and Power Grids." which is pretty much the topic which has been the area of his doctoral specialization.

The background of the research is the emerging trends of the energy supply which is no longer in hierarchical setup of up down (e.g. from the source to household) distribution, but also in the reversal direction. So that not only the source can sell their energy to the downstream but also the downstream line if they produce energy, the downstream costumer can also sell their energy to the upstream.

This issue of developing two ways of distribution is explained pretty much from different perspectives. But especially, since the energy transaction mechanism between the upstream and the downstream mechanism is built in the agent based point of view and also the mechanism of transaction is involving auction mechanism, so a lot of things what Carsten have applied in his research pretty much coherent with the LARGE field. This session off course has brought big lesson fur the group.

The discussion of this reversible energy transaction is pretty much done in multiple perspectives, since in this session a lot of people come from different area of specialization (e.g. economics, business network, information and decision sciences, etc). Carsten did an excellent explanation also to the multi-background viewer’s questions.

Since Carsten have a strong computer science background, he also shared some of his experiences (e.g. tool for development) in the development of his project. He introduced us a new tools for faster software development like grailsTM and make some demo also about what the platform advances. This session was very successful and everybody was enthusiastic.

Meeting Minutes - 12 November 2008

Today Uzay Kaymak gave a presentation on the Ming Hua and Nicholas Jenning's paper of "Designing a Successful Trading Agent: A Fuzzy Set Aproach".

The paper tells us about the writers experience in implementing fuzzy logics in their SouthamptonTAC agent.

SouthhamptonTAC agent participated successfully in the first and second (TAC) Trading Agent Competition, a competition which facilitates the competition of the participant's "travel" agents in fulfilling their customer demand of travel package (flight ticket, hotel, and extra entertainment ticket). Those participating agents should compete with each other in different (flight ticket, hotel, and extra entertainment ticket) auctions provided by the TAC platform to statisfy the demand of the agent's costumer.

Uzay briefly explains how the SouthamptonTAC implements the fuzzy logics in the hotel, ticket, and entertaiment bidding actions. He (Uzay) found it quiet surprising how the performance of the fuzzy rules implementation can work well on the competition.

The presentation, is wrapped up by a small discussion about the competition conditions. Wolf gives the audience some description about the condition of the TAC competition (The rounds, the finalists, etc.).

Meeting Minutes - 15 October 2008

Katalin led a discussion on the agent definitions and agent properties. She presented first a number of definitions from the literature mainly based on the article by Franklin and Gaesser. We compared and analyzed these definitions, especially w.r.t. to their properties and also discussed how these relate to the broad definition by Norvig and Russel.

It seems that there are three types of agents defined: “agents”, “autonomous agents” and “intelligent agents”. It is not always clear, however whether authors mean in fact agents with different properties when using these notions. Autonomy is a basic property that seemingly all agents should possess. However, we couldn’t really agree on what autonomy means. Explanations we gave varied from the notion of being “independent” to being “goal-oriented”, “pro-active”. The conclusion is that there are different degrees of autonomy.

With respect to the agents properties it also seems that learning and adaptation are sometimes interchanged.

In the second part of the meeting the CAS (Complex Adaptive System) definition has been focused, and agent-based modeling as an approach. Here the focus is on agents as interactive individual entities, and the emergent system properties. Agents are classified in three classes based on the rules they use and their level of adaptation: simple agents, complex-rule agents and advanced-rule agents.

Katalin concludes that we shouldn’t really bother about the many definitions; there are agents with different properties and different complexities. What should be important, however, is that authors give a clear description of what they call an “agent” in their paper, which properties the agents have, with definition of these properties.

Meeting Minutes - 21 July, 2008

Today Peter Berends held a presentation discussing the case “The Application of Intelligent Personalised Agents for Buyer Decision Empowerment at the Dutch Flower Auction (DFA)”.

The DFA consists of six individual auctions located throughout the Netherlands which each host a couple of clock auctions on which goods are auctioned. In total there are 39 clocks on the DFA.

When buying through a clock auction, buyers aim to buy at the lowest price. Thus, they try to show their interest at the very latest possible moment. However, caution needs to be exercised because reacting too late means forgoing the ability to buy the auctioned product because other buyers might have jumped on the opportunity. As a consequence, buying through clock auctions is not an easy job. There is also a remote application through which buyers can buy at the DFA from any location. Many buyers procure for their customers who are located at different locations, and there are six different auctions of the DFA in the Netherlands. This means that when remotely buying, buyers could optimise the transportation cost and transportation time by making sure they buy products from an auction that is in close proximity to the location where they need to ship the goods.

This means that there are four main decision parameters to be considered with every buy: (1) price; (2) quality measures; (3) transportation costs; and (4) transportation time. There was a discussion on how intelligent agent-based systems could be utilized to empower buyers in their decision making.

After the presentation a discussion was held about where best to apply the agents, their benefits, and the information the agents need to base its decision on.

Meeting Minutes - 23 June, 2008

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.

Next LARGE meeting is scheduled for 21 July.

Meeting Minutes - 16 June, 2008

Elfriede Krauth presented a paper (together with Wolf Ketter and Jacqueline Bloemhof ) on Business Intelligence: Academic vs. Industry Perspective. Dashboards are distinguishing features between the disciplines.