Ryszard Kowalczyk

Bio: Ryszard Kowalczyk is an academic researcher from Swinburne University of Technology. The author has contributed to research in topics: Negotiation & Cloud computing. The author has an hindex of 30, co-authored 253 publications receiving 3439 citations. Previous affiliations of Ryszard Kowalczyk include Polish Academy of Sciences & Commonwealth Scientific and Industrial Research Organisation.

Intelligent agents for automated one-to-many e-commerce negotiation

Abstract: Negotiation is a process in which two or more parties with different criteria, constraints, and preferences, jointly reach an agreement on the terms of a transaction. Many current automated negotiation systems support one-to-one negotiation. One-to-many negotiation has been mostly automated using various kinds of auction mechanisms, which have a number of limitations such as the lack of the ability to perform two-way communication of offers and counteroffers. Moreover, in auctions, there is no way of exercising different negotiation strategies with different opponents. Even though auction-based online trading is suitable for many applications, there are some in which there is a need for such greater flexibility. There has been a significant body of work towards sophisticated one-to-one automated negotiation. In this paper, we present a framework for one-to-many negotiation by means of conducting a number of concurrent coordinated one-to-one negotiations. In our framework, a number of agents, all working on behalf of one party, negotiate individually with other parties. After each negotiation cycle, these agents report back to a coordinating agent that evaluates how well each agent has done, and issues new instructions accordingly. Each individual agent conducts reasoning by using constraint-based techniques. We outline two levels of strategies that can be exercised on two levels, the individual negotiation level, and the coordination level. We also show that our one-to-many negotiation architecture can be directly used to support many-to-many negotiations. In our prototype Intelligent Trading Agency (ITA), agents autonomously negotiate multi- attribute terms of transactions in an e-commerce environment tested with a personal computer trading scenario.

Dynamic analysis of multiagent Q-learning with ε-greedy exploration

Abstract: The development of mechanisms to understand and model the expected behaviour of multiagent learners is becoming increasingly important as the area rapidly find application in a variety of domains. In this paper we present a framework to model the behaviour of Q-learning agents using the e-greedy exploration mechanism. For this, we analyse a continuous-time version of the Q-learning update rule and study how the presence of other agents and the e-greedy mechanism affect it. We then model the problem as a system of difference equations which is used to theoretically analyse the expected behaviour of the agents. The applicability of the framework is tested through experiments in typical games selected from the literature.

On Constraint-Based Reasoning in e-Negotiation Agents

Abstract: Negotiation typically involves a number of parties with different criteria, constraints and preferences that determine the individual areas of interest, i.e. the range and order of the preferred solutions of each party. The parties usually have a limited common knowledge of each other's areas of interest. Therefore a range of possible agreements, i.e. the common area of interest is typically not known to the parties a priori. In order to find a mutual agreement the parties explore possible agreements by the process of exchanging information in the form of offers. During the negotiation process the range of possible offers of each party changes according to the current information available. As negotiation progresses and more information become available the ranges reduce until an agreement can be found or the parties withdraw from negotiation. This interpretation allows one to consider the negotiation problem as a constraint satisfaction problem and the negotiation process as constraintbased reasoning. This paper presents some aspects of that interpretation. In particular it outlines the constraint-based representation and constraint propagation mechanisms used in an experimental system of e-Negotiation Agents (eNAs). The eNAs can autonomously negotiate the multi-issue terms of transactions in an e-commerce environment tested with the used car trading problem.

A coordinated architecture for the agent-based service level agreement negotiation of Web service composition

Abstract: Recent progress in the field of Web services has made it possible to integrate inter-organizational and heterogeneous services on the Web at runtime If a user request cannot be satisfied by a single Web service, it is (or should be) possible to combine existing services in order to fulfill the request However, there are several challenging issues that need to be addressed before this can be realized in the true sense One of them is the ability to ensure end-to-end QoS of a Web service composition There is a need for a SLA negotiation system which can ensure the autonomous QoS negotiation of Web service compositions irrespective of the application domain In this paper we propose agent-based coordinated-negotiation architecture to ensure collective functionality, end-to-end QoS and the stateful coordination of complex services We describe a prototype implementation to demonstrate how this architecture can be used in different application domains We have also demonstrated how the negotiation system on the service provider's side can be implemented both as an agent based negotiation system and as a Web service based negotiation system

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).