Dionisis D. Kehagias

Intelligent policy recommendations on enterprise resource planning by the use of agent technology and data mining techniques

Abstract Enterprise Resource Planning systems tend to deploy Supply Chain Management and/or Customer Relationship Management techniques, in order to successfully fuse information to customers, suppliers, manufacturers and warehouses, and therefore minimize system-wide costs while satisfying service level requirements. Although efficient, these systems are neither versatile nor adaptive, since newly discovered customer trends cannot be easily integrated with existing knowledge. Advancing on the way the above mentioned techniques apply on ERP systems, we have developed a multi-agent system that introduces adaptive intelligence as a powerful add-on for ERP software customization. The system can be thought of as a recommendation engine, which takes advantage of knowledge gained through the use of data mining techniques, and incorporates it into the resulting company selling policy. The intelligent agents of the system can be periodically retrained as new information is added to the ERP. In this paper, we present the architecture and development details of the system, and demonstrate its application on a real test case.

A Framework for Constructing Multi-agent Applications and Training Intelligent Agents

As agent-oriented paradigm is reaching a significant level of acceptance by software developers, there is a lack of integrated high-level abstraction tools for the design and development of agent-based applications, In an effort to mitigate this deficiency, we introduce Agent Academy, an integrated development framework, implemented itself as a multi-agent system, that supports, in a single tool, the design of agent behaviours and reusable agent types, the definition of ontologies, and the instantiation of single agents or multi-agent communities. In addition to these characteristics, our framework goes deeper into agents, by implementing a mechanism for embedding rule-based reasoning into them. We call this procedure agent training and it is realized by the application of AI techniques for knowledge discovery on application-specific data, which may be available to the agent developer. In this respect, Agent Academy provides an easy-to-use facility that encourages the substitution of existing, traditionally developed applications by new ones, which follow the agent-orientation paradigm.

Information agents cooperating with heterogenous data sources for customer-order management

As multi-agent systems and information agents obtain an increasing acceptance by application developers, existing legacy Enterprise Resource Planning (ERP) systems still provide the main source of data used in customer, supplier and inventory resource management. In this paper we present a multi-agent system, comprised of information agents, which cooperates with a legacy ERP in order to carry out orders posted by customers in an enterprise environment. Our system is enriched by the capability of producing recommendations to the interested customer through agent cooperation. At first, we address the problem of information workload in an enterprise environment and explore the opportunity of a plausible solution. Secondly we present the architecture of our system and the types of agents involved in it. Finally, we show how it manipulates retrieved information for efficient and facile customer-order management and illustrate results derived from real-data.

Efficient E-Commerce Agent Design Based on Clustering eBay Data

In this paper we present the use of data mining techniques on data derived from eBay auctions in order to design a decision making mechanism for autonomous agents. These agents operate as parallel threads that concurrently receive data from multiple online auctions, where a specific item is traded. The agent objective is to select the auction, in which the traded item will be sold at the lowest price. Trading agents are armed with a realtime decision-making mechanism, adaptable to changes occurring in the auctions environment. The paper shows how a particular data mining technique can be incorporated into the design of trading agents and provides the theoretical basis of the agent decision mechanism.

Specification of information needs for the development of a mobile communication platform to support mobility of people with functional limitations

Opportunities for people with functional limitations are increasing. ICT provides a number of possibilities to receive care, to travel, to work, to educate oneself, to inform oneself and to meet other people. In this paper, the methodology for defining user requirements for supporting people with functional limitations through ICT (the ASK-It Concept) is presented. The methodology covers various domains. A case example as an illustration of the process is used: a communication platform to support social relations and communities. The methodology is built upon the definition of user groups, the elaboration and implementation of relevant action and activity theory principles, and is successively developed with the content modelling procedure, in order to provide a formal description of user information needs in a computer understandable and interoperable format.

A long-term profit seeking strategy for continuous double auctions in a trading agent competition

This paper presents a new bidding strategy for continuous double auctions (CDA) designed for Mertacor, a successful trading agent, which won the first price in the “travel game” of Trading Agent Competition (TAC) for 2005. TAC provides a realistic benchmarking environment in which various travel commodities are offered in simultaneous online auctions. Among these, entertainment tickets are traded in CDA. The latter, represent the most dynamic part of the TAC game, in which agents are both sellers and buyers. In a CDA many uncertainty factors are introduced, because prices are constantly changing during the game and price fluctuations are hard to be predicted. In order to deal with these factors of uncertainty we have designed a strategy based on achieving a pre-defined long-term profit. This preserves the bidding attitude of our agent and shows flexibility in changes of the environment. We finally present and discuss the results of TAC-05, as well as an analysis of agents performance in the entertainment auctions.

Designing Pricing Mechanisms for Autonomous Agents Based on Bid‐Forecasting

Autonomous agents that participate in the electronic market environment introduce an advanced paradigm for realizing automated deliberations over offered prices of auctioned goods. These agents represent humans and their assets, therefore it is critical for them not only to act rationally but also efficiently. By enabling agents to deploy bidding strategies and to compete with each other in a marketplace, a valuable amount of historical data is produced. An optimal dynamic forecasting of the maximum offered bid would enable more gainful behaviours by agents. In this respect, this paper presents a methodology that takes advantage of price offers generated in e‐auctions, in order to provide an adequate short‐term forecasting schema based on time‐series analysis. The forecast is incorporated into the reasoning mechanism of a group of autonomous e‐auction agents to improve their bidding behaviour. In order to test the improvement introduced by the proposed method, we set up a test‐bed, on which a slightly var...

A WSDL structure based approach for semantic categorization of web service elements

This paper introduces a semantic categorization technique whose goal is to classify web service structural elements into their semantically described counterparts The semantic representations of the web service elements are provided by a set of predefined ontologies Our technique proposes a three layer categorization approach based on the WSDL document structure, i.e its operations and input/output parameters By means of the proposed categorization technique, automatic web service invocation is enabled in addition to semantic annotation of web service Apart from presenting the theoretical details of the semantic categorization technique, this paper conducts preliminary assessment of its performance in terms of classification accuracy and compares it to one of the most known and successful web service classification tools.

Implementing an open reference architecture based on web service mining for the integration of distributed applications and multi-agent systems

This paper introduces an open reference architecture that enables seamless integration of distributed applications and agent-based systems. The reference architecture has been designed in order to be holistic, open and standards-abiding. It is based on an ontological framework that operates as a middleware between application developers and service providers on the one side and multiagent systems on the other one. The proposed architecture enables seamless integration of closed (standalone) applications, provided that these applications export their functionality in the form of public web services. Moreover, we propose a data mining framework that operates on web service data and performs classification of web services and their operations into their semantically described counterparts. This enables seamless integration of applications through the corresponding web service interfaces, based on the ontological framework. On the other hand we show that knowledge derived from web service mining can be used by multi-agent systems in order to provide composite functionalities derived from the distributed web service operations. Moreover, agents are capable to provide personalized services that fulfill user requirements, by taking into account the personal profile, as well as the history of the end user. The paper presents the prototype tools that have been implemented for the realization of the proposed reference architecture.

User needs for mobility improvement for people with functional limitations

According to the Eurostat statistics, 25.3% of the European Union (15 countries) population are “severely hampered” (9.3%) or “hampered to some extent” (16.0%). More specifically, these figures refer to “hampered in daily activities by any physical or mental health problem, illness or disability” (Simoes and Gomez, 2005; United Nations, 2003). Their quality of life would improve substantially if they could participate more actively in the society, while society itself could benefit from this contribution. The transdisciplinary project ASK-IT aims to support this and is developing an ambient intelligence system that provides information to people with functional limitations.