In the LARGE meeting, Yixin presented the tutorial paper about preference handeling by Brafman&Domshlak.
In the first session (Nov. 11, 2009), we focused on models and languages for representing preferences. Starting with the quantitative languages, we discussed about two important classes of value functions: Additively Independent (AI) and Generalized Additively Independent (GAI) functions, as well as some nice properties of the corresponding representation structure. Despite of the computational efficiency for preference comparison or ordering, those quantitative languages put too much cognitive burden on the users' slide and they are usually difficult for users to reflect upon. Hence we need somewhat more "easy'' languages which, hopefully, carry with them desirable properties from both cognition and computation aspects. Given these considerations, a natural choice would be taking generalizing preference expressions. When equipped with CP-nets, such quanlitative language can handel different kinds of queries very efficiently.
In the second session (Nov. 18, 2009), we started with some discussion about preference compilation techniques, namely, structure-based and structure-free. Both aim to combine the quantitative and qualitative models in such a way that we can map diverse statements into a single, well-understood representation. Then, we had a quick look at uncertainty and utility functions. During the rest part of the meeting, we focused on preference specification and elicitation, discuessed about different methods which include "prior-based'' ones such as maximum likelihood and Bayesian reasoning, as well as ''prior-free" minmax regret method. For elicitation, since it usually works in a sequential way, we need to come up with either nice heuristics to determine the optimal sequence of queries. Or if the computational price is not a big concern, we can use the elegant partially observable Markov decision process (POMDP) model.
The paper provides a very nice framework of preference handeling. In the nex few months, we will study the existing methods for preference elicitation and representation and ideally be able to construct a more concrete framework to model preferences in energy market.
Wednesday, November 18, 2009
Tuesday, November 3, 2009
Dutch Flower Auction Recommender Agent
Last LARGE meeting, I (Meditya Wasesa) presented the status of the Recommender Agent development in the Dutch Flower Auction domain.
I reported that we have finalized the first step of data exploration, that is meant to elicit all determining factors which significantly influence the revenue of a good in Dutch Flower Auction domain. Our exploratory research infers that there are several auction design parameters which the auctioneer can adjust to control the level of revenue.
Knowing these auction design parameters, we have developed an analytical that we will drive the logic of the future recommender agent. At future we will implement our analytical model to a recommendation agent and will test it with simulation, lab experiments, and field experiments.
I reported that we have finalized the first step of data exploration, that is meant to elicit all determining factors which significantly influence the revenue of a good in Dutch Flower Auction domain. Our exploratory research infers that there are several auction design parameters which the auctioneer can adjust to control the level of revenue.
Knowing these auction design parameters, we have developed an analytical that we will drive the logic of the future recommender agent. At future we will implement our analytical model to a recommendation agent and will test it with simulation, lab experiments, and field experiments.
Tuesday, October 20, 2009
Decision coordination in a supply-chain agent
Why is decision coordination an important problem? Decision support is especially critical when human decision makers are faced with combinatorial problems, uncertain and partially-visible data, and our built-in cognitive biases. When the results of decisions in different domains interact with each other, we have an additional level of complexity that is easy to ignore.
We use the TAC SCM simulation to model complex, interacting decisions, and the fully-autonomous MinneTAC trading agent to test our ideas for decision support. The agent must trade in two competitive markets simultaneously, while managing its own inventory and production facility. A successful agent must buy parts it needs to manufacture and sell finished products, and it must sell its finished products at a profit. Inventory is relatively costly, competition is stiff, and agents that cannot effectively coordinate their decisions are easily defeated.
We reviewed the coordination strategies employed by a number of successful agents. These include a "sales pull" method, inventory-centric approaches, a production-centric approach that fills its future production schedule with the products that are expected to give the highest marginal profit, an approach that projects a "target demand" into the future that is expected to satisfy profit targets, and a multi-layer system of internal "markets" in which projected customer demand bids on products, which in turn bid on parts and production capacity, etc.
We wrapped up by looking in some detail at the behaviors of the two top agents in the 2009 competition, DeepMaize from the University of Michigan and TacTex from the University of Texas. They were very nearly tied, although TacTex bought and sold considerably more volume and carried much larger inventories. We looked at an example where TacTex built up a large finished-goods inventory during a period of low customer demand, when parts are inexpensive, and used that inventory to keep prices depressed during a later period of high demand, when other agents were competing for parts and were consequently squeezed for profits. This is clearly a very risky strategy, but we assume that the TacTex team has used its machine-learning expertise to recognize the market signal patterns that indicate a reasonable probability of such a situation occuring. The regime model used by MinneTAC could presumably predict such a situation also, but so far it's not being used to drive strategic decisions.
We use the TAC SCM simulation to model complex, interacting decisions, and the fully-autonomous MinneTAC trading agent to test our ideas for decision support. The agent must trade in two competitive markets simultaneously, while managing its own inventory and production facility. A successful agent must buy parts it needs to manufacture and sell finished products, and it must sell its finished products at a profit. Inventory is relatively costly, competition is stiff, and agents that cannot effectively coordinate their decisions are easily defeated.
We reviewed the coordination strategies employed by a number of successful agents. These include a "sales pull" method, inventory-centric approaches, a production-centric approach that fills its future production schedule with the products that are expected to give the highest marginal profit, an approach that projects a "target demand" into the future that is expected to satisfy profit targets, and a multi-layer system of internal "markets" in which projected customer demand bids on products, which in turn bid on parts and production capacity, etc.
We wrapped up by looking in some detail at the behaviors of the two top agents in the 2009 competition, DeepMaize from the University of Michigan and TacTex from the University of Texas. They were very nearly tied, although TacTex bought and sold considerably more volume and carried much larger inventories. We looked at an example where TacTex built up a large finished-goods inventory during a period of low customer demand, when parts are inexpensive, and used that inventory to keep prices depressed during a later period of high demand, when other agents were competing for parts and were consequently squeezed for profits. This is clearly a very risky strategy, but we assume that the TacTex team has used its machine-learning expertise to recognize the market signal patterns that indicate a reasonable probability of such a situation occuring. The regime model used by MinneTAC could presumably predict such a situation also, but so far it's not being used to drive strategic decisions.
Friday, May 29, 2009
Meeting Minutes - 27 May 2009
At 27th May 2009, we have 2 guest researchers, Xiaoyu Mao and Ducco Ferro, from Almende, a research company that focusses it research in multi-agent system. Both were discussing their papers that they will present at AAMAS.
Xiaoyu Mau presented an application of agent technology in the airport operation entitled "Heterogeneous MAS Scheduling for Airport Ground Handling", while Ducco presented his paper entitled "The Windmill Method for Setting up Support for Resolving Sparse Incidents in Communication Networks". That was a great meeting with great crowd also.
Xiaoyu Mau presented an application of agent technology in the airport operation entitled "Heterogeneous MAS Scheduling for Airport Ground Handling", while Ducco presented his paper entitled "The Windmill Method for Setting up Support for Resolving Sparse Incidents in Communication Networks". That was a great meeting with great crowd also.
Thursday, May 7, 2009
Meeting Minutes - 6 May 2009
At 6th of May, we have a guest speaket from TU-Delft. His name is Mathijs de Weerdt. He presented his ACM paper regarding the Qualitative Vickrey Auction concept. A concept at which bidders and auctioneers (centers by his definition) put the agreement of the bid not based on the monetary value (highest or lowest price) but based on the offer demand attributes suitability rank.
He explained this concept in the reversed auction environment example. A concept at which the auctioneer has already defined the rank of offers preferences (the offer contains of many attributes). And the bidder that submits the offer of highest rank wins. The winner bidder then can choose to deliver the offer ranges from his bid point offer to the second highest bidder point offer (Vickrey Auction).
Nice concept, based on the attributes rank preferences. For more explanation you are welcomed to browse our site to download the paper directly (http://large.rsm.nl/meetings.xml).
He explained this concept in the reversed auction environment example. A concept at which the auctioneer has already defined the rank of offers preferences (the offer contains of many attributes). And the bidder that submits the offer of highest rank wins. The winner bidder then can choose to deliver the offer ranges from his bid point offer to the second highest bidder point offer (Vickrey Auction).
Nice concept, based on the attributes rank preferences. For more explanation you are welcomed to browse our site to download the paper directly (http://large.rsm.nl/meetings.xml).
Wednesday, April 22, 2009
Meeting Minutes - 22 April 2009
Today I (Meditya) presented a Herbert A. Simon's Paper entitled "Machine as Mind" appeared in "Android Epistemology" by C.Glymour, K.Ford, and P.Hayes.
Defining that the primitives of mind consist of, Symbols, Complex Structures of Symbols, and Processes that Operate on Symbols (Newell & Simon, 1976), the central thesis of his writings states, "Conventional computers can be, and have been programmed to represent symbol structures and carry out processes on those structures in a manner that parallels, step by step, the way human brain does it.".
In his writings he (Simon) basically argued that every angle of human mind is translable into definable representations, and the definition can be translated and embedded to a thinking machine. In order to defend his thesis, he explained several important points of disscussion such as:
- The Concept of Decomposable System
- Two Approaches of Artificial Intelligence (humanoid and non humanoid)
- The Concept of Mind from Psychological Perspective
- Selective Heuristic Search
- Recognition: The Indexed Memory
- Seriality: The Limits of Attention (short term memory)
- The Architecture of Expert System
In addition he also responded and defended his thesis to the disputes that people usually have regarding the angle of thinking that machine could not copy such as:
- Semantics
- Intention
- "Ill Structured" Tasks
- Language Processing
- Intuition
- Insight
- Creativity
At the end of this meeting we have a nice discussion about the state of the art of this writing (1995) and the condition that is happening now, and the extent of the applicability of the Simon's vision in the present and the future.
Defining that the primitives of mind consist of, Symbols, Complex Structures of Symbols, and Processes that Operate on Symbols (Newell & Simon, 1976), the central thesis of his writings states, "Conventional computers can be, and have been programmed to represent symbol structures and carry out processes on those structures in a manner that parallels, step by step, the way human brain does it.".
In his writings he (Simon) basically argued that every angle of human mind is translable into definable representations, and the definition can be translated and embedded to a thinking machine. In order to defend his thesis, he explained several important points of disscussion such as:
- The Concept of Decomposable System
- Two Approaches of Artificial Intelligence (humanoid and non humanoid)
- The Concept of Mind from Psychological Perspective
- Selective Heuristic Search
- Recognition: The Indexed Memory
- Seriality: The Limits of Attention (short term memory)
- The Architecture of Expert System
In addition he also responded and defended his thesis to the disputes that people usually have regarding the angle of thinking that machine could not copy such as:
- Semantics
- Intention
- "Ill Structured" Tasks
- Language Processing
- Intuition
- Insight
- Creativity
At the end of this meeting we have a nice discussion about the state of the art of this writing (1995) and the condition that is happening now, and the extent of the applicability of the Simon's vision in the present and the future.
Wednesday, April 15, 2009
Meeting Minutes - 15 April 2009
Today I presented the paper “Computational Intelligence in Economic Games and Policy Design” by Herbert Dawid, Han La Poutré, and Xin Yao (IEEE Computational Intelligence Magazine, 3(4), 22–26, 2008). The paper provides an overview of applications of computational intelligence techniques in economics. Both strong and weak points of the use of computational intelligence techniques are discussed. According to the authors, the two most important weak point are the issue of empirical validation and the issue of robustness.
I also discussed my own view on the relation between mainstream economics on the one hand and agent-based computational economics on the other hand. Due to the increasing popularity of experimental economics and evolutionary game theory, mainstream economics focuses more and more on bounded rationality and dynamic (rather than static) analysis. From this perspective, the difference between mainstream economics and agent-based computational economics is smaller than is sometimes thought. I argued that the main difference is between following a mathematical approach (as mainstream economics does) and following a simulation approach (as agent-based computational economics does). There is much to be gained by combining these two approaches. Today’s meeting ended with a discussion of the difference between mathematical analysis and computer simulation, and how this difference relates to the difference between deduction and induction in science. We also discussed the issue of implicit assumptions that are hidden in technical details in agent-based computational economics research.
I also discussed my own view on the relation between mainstream economics on the one hand and agent-based computational economics on the other hand. Due to the increasing popularity of experimental economics and evolutionary game theory, mainstream economics focuses more and more on bounded rationality and dynamic (rather than static) analysis. From this perspective, the difference between mainstream economics and agent-based computational economics is smaller than is sometimes thought. I argued that the main difference is between following a mathematical approach (as mainstream economics does) and following a simulation approach (as agent-based computational economics does). There is much to be gained by combining these two approaches. Today’s meeting ended with a discussion of the difference between mathematical analysis and computer simulation, and how this difference relates to the difference between deduction and induction in science. We also discussed the issue of implicit assumptions that are hidden in technical details in agent-based computational economics research.
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