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Εθνικό και Καποδιστριακό Πανεπιστήμιο Αθηνών. On the Use of Optimization Techniques for Strategy Definition in Multi Issue Negotiations Κυριακή Παναγίδη Επιβλέπων Καθηγητής: Ευστάθιος Χατζηευθυμιάδης. 1/. Contents . Definitions Electronic Commerce Intelligent Software Agents
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Εθνικό και Καποδιστριακό Πανεπιστήμιο Αθηνών On the Use of Optimization Techniques for Strategy Definition in Multi Issue Negotiations Κυριακή Παναγίδη Επιβλέπων Καθηγητής: Ευστάθιος Χατζηευθυμιάδης 1/
Contents • Definitions • Electronic Commerce • Intelligent Software Agents • Electronic Marketplaces • Negotiations • Problem Definition • Strategy Definition • Proposed Algorithms • Experiments • Conclusions • Future Work 1/
“Optimization as ageless as time” …
Electronic Commerce Electronic Commerce (E-Commerce) is defined by the Electronic Commerce Association as: • “any form of business or administrative transaction or information exchange that is executed using any information and communications technology” . • “business practice related to buying and selling goods, products or services, in the Internet” 1/ 1/
Intelligent Software Agents - IAs • Intelligent software agents are programs acting on behalf of their human users” • “Intelligent software contains features as perception, interpretation of natural language, learning and decision making” • “A piece of software which performs a given task using information gleaned from its environment to act in a suitable manner so as to complete the task successfully. The software should be able to adapt itself based on changes occurring in its environment, so that a change in circumstances will still yield the intended result.” • “Software agents carry out certain operations on behalf of a user or another program with some degree of independence or autonomy combined with a set of goals or tasks for which they are designed” • “Intelligent Agents are computerized servants, it is software that communicates, cooperates and negotiates with each other. They have the ability to take over human tasks and interact with people in human like ways. They are bringing technology into a new dimension simplifying the use of computers, allowing humans to move away from complex programming languages creating a more human interaction” Degree of reasoning As a third party 1/
Intelligent Software Agents - IAs Characteristics the IA should have the ability to modify the human user requests and ask for additional information or clarifications what the user needs how is going to satisfy user dynamically assess which actions to execute and when Accept the user’s statement of goals and carry out the task delegated to it interact with other IAs, programs or humans take initiatives recognize the user’s preference Try to do what is asked for and act in order to achieve the user’s goals 1/
Intelligent Software Agents- IAs Barriers: • IAs should have access to their catalogues. • User goals have to be specified. • Users have to obtain information such as prices, product’s issues, returning policies, delivery time, • Security problems may occur when submitting sensitive information 1/
Electronic Marketplaces “Virtual location where entities that are not known in advance can cooperate in order to achieve common goals. These entities have their own preferences and strategies” Most of the proposed E-marketplace’s models are classified in the following two categories: • Direct transactions among providers and consumers • IA-based brokered transactions 1/
Automated Negotiations “a decentralized decision-making process used to search and arrive at an agreement that satisfies the requirements of two or more parties in the presence of limited common knowledge and conflicting preferences.” “the process where entities try to agree upon the exchange of a product or as a mean of compromise, in order to reach mutual agreements.” 1. Electronic automated negotiation systems (EANSs) 2. Negotiation support systems (NSSs) 1/
Electronic Negotiations Properties in Automated Negotiations: • Simplicity • Efficiency • Distribution • Symmetry • Stability • Flexibility 1/
Complexity of Human behavior Electronic Negotiations-Problems Real Life Negotiation Problems Ill defined Information not equally distributed Participants with partial knowledge Communication is ambiguous or imprecise • Multiple issues negotiation • Similar product suggestion • Correlated product suggestion • Ultimatum • Negotiation cost • Learning 1/
Problem Definition Buyer Driven Simultaneous One-to-many No knowledge No Coordinator
Problem Definition • Product • has a number of issues that increase or decrease each player’s utility. An example : • Price • Delivery time • Quality of Service (QoS) • Seller’s trust
Problem Definition Buyer Driven Simultaneous One-to-many No knowledge Multi Issue No Coordinator
Problem Definition Goal : Choose the best agreement Problem is rising: “How do we evaluate two or more deals with different issues/sets?” 1/
Problem Definition Buyer i is in “worst case” • Price • QoS 1/
Problem Definition “How do we evaluate two or more deals with different issues/sets?” Answer: Utility Restrictions: • Proportional/ Not Proportional • Ultimatum 1/
Strategy Definition Weights Definition Space Converging Solve our problem like a mathematical problem, in which we change the weights of issues involved in negotiation Studied algorithms: • Heuristic • Simplex • Analytical Hierarchy Process Solve Like in nature we assume our buyers like particles moving in space Studied algorithms: • Combination of Particle Swarm Optimization and Virtual Force 1/
Weights Definition- Heuristic Method Comparison between the values of issues of buyeri and the values of issues of the agreement. Each issue then is characterized as an issue that needs a change or not
Weights Definition- Simplex Method Maximize Output Input Wi Vi, Vagreement Restrictions
A= Weights Definition- Analytic Hierarchy Process
Space Converging-PSO with VFA • Uses a number of IAs (particles) that constitute a swarm moving around in the search space looking for the best solution • Each particle in search space adjusts its “flying” according to its own flying experience as well as the flying experience of other particles
Space Converging-PSO with VFA Each particle adjusts its travelling speed dynamically corresponding to the flying experiences of itself and its colleagues Each particle modifies its position according to: • its current position • its current velocity • the distance between its current position and pbest • the distance between its current position and gbest
Space Converging-PSO with VFA Particles = Buyers bargaining a set of product Cannot be presented by a set o 2 coordinates (x,y) • VFA algorithm Every product is a vector [V1,V2,…Vn] Particle is moving in N-dimensional space
Space Converging-PSO with VFA next position xi(t+1) depends from the velocity vi(t), which is equal to where and c1, c2 are random generated values.
Experiments - Performance Metrics The agreement ratio (AG) Average Buyer Utility (ABU) Average Seller utility (ASU)
Experiments - Performance Metrics Average Rounds (AR) Number of successful thread (Pt) Fairness (F)
Experiments Set of experiments • 300 negotiations NT= 50, I = 4 and V in [10, 300] (450.000) • 300 negotiations , V = 100 NT= 50 and I=2k, where k=2,…,5. • 500 negotiations: V = 100, I = 4 and NT 5 in [5,50]. *Seller’s cost is randomly selected in the interval [10, 50].
Conclusions • The basic idea :an algorithm which can deal with • one-to-many, • concurrent, • dynamic • with limited knowledge negotiations • Heuristic, Simplex and AHP methods, redefine the weights of product’s • Moving IAs in the N-dimensional space applying the Particle Swarm Optimization algorithm (PSO) combined with VFA. • The average utility gained by the buyer in all methods is above 50%. • PSO algorithm can handle excellent • a large number of issues and • a large number of IAs.
Future Work • Relevant function for dynamically change of weights for the seller’s part • The following step for PSO algorithm is to study whether the behavior of particles will change, if the weights of issues can be dynamically defined again during the negotiations. • The comparison of our results with real data would give us more realistic perspective between the developed methods providing us with the “closest-to-human-behavior” methodology
Thank you for your attention! 27/28 1/
Questions; 28/28
