In this article, we present a last-minute travel application as part of a complete virtual travel agency. Each year, a significant amount of tour packages are sold as last minute tours in Germany. The last-minute travel application presented here uses case-based reasoning to bridge this gap and simulate the sales assistance of a human travel agent. Important for the acceptance by customers is also the integration into the virtual travel agency and the interconnections to other parts of this system, such as background information or the online car rental application.

In this article, we present a last-minute travel application as part of a complete virtual travel agency. Each year, a significant amount of tour packages are sold as last minute tours in Germany. It is impossible for a travel agent to keep track of all the offered tour packages. Electronic-commerce applications might present the best possible tour package for a specific customer request. Traditional database-driven applications, as used by most of the tour operators, are not sufficient enough to implement a sales process with consultation on the World Wide Web. The last-minute travel application presented here uses case-based reasoning to bridge this gap and simulate the sales assistance of a human travel agent. A case retrieval net, as an internal data structure, proved to be efficient in handling the large amount of data. Important for the acceptance by customers is also the integration into the virtual travel agency and the interconnections to other parts of this system, such as background information or the online car rental application.

The KDD conference series is the primary venue for presenting and publishing the highest quality advances in the field.

The AAAI-99 Workshop Program (a part of the sixteenth national conference on artificial intelligence) was held in Orlando, Florida. Each workshop was limited to approximately 25 to 50 participants. Participation was by invitation from the workshop organizers. The workshops were Agent-Based Systems in the Business Context, Agents' Conflicts, Artificial Intelligence for Distributed Information Networking, Artificial Intelligence for Electronic Commerce, Computation with Neural Systems Workshop, Configuration, Data Mining with Evolutionary Algorithms: Research Directions (Jointly sponsored by GECCO-99), Environmental Decision Support Systems and Artificial Intelligence, Exploring Synergies of Knowledge Management and Case-Based Reasoning, Intelligent Information Systems, Intelligent Software Engineering, Machine Learning for Information Extraction, Mixed-Initiative Intelligence, Negotiation: Settling Conflicts and Identifying Opportunities, Ontology Management, and Reasoning in Context for AI Applications.

The AAAI-99 Workshop Program (a part of the sixteenth national conference on artificial intelligence) was held in Orlando, Florida. The program included 16 workshops covering a wide range of topics in AI. Each workshop was limited to approximately 25 to 50 participants. Participation was by invitation from the workshop organizers. The workshops were Agent-Based Systems in the Business Context, Agents' Conflicts, Artificial Intelligence for Distributed Information Networking, Artificial Intelligence for Electronic Commerce, Computation with Neural Systems Workshop, Configuration, Data Mining with Evolutionary Algorithms: Research Directions (Jointly sponsored by GECCO-99), Environmental Decision Support Systems and Artificial Intelligence, Exploring Synergies of Knowledge Management and Case-Based Reasoning, Intelligent Information Systems, Intelligent Software Engineering, Machine Learning for Information Extraction, Mixed-Initiative Intelligence, Negotiation: Settling Conflicts and Identifying Opportunities, Ontology Management, and Reasoning in Context for AI Applications.

The Fifteenth National Conference on Artificial Intelligence (AAAI-98) was held in Madison, Wisconsin, on 26-30 July. The following four workshops were held in conjunction with the conference: (1) Case-Based Reasoning Integrations, (2) Learning for Text Categorization, (3) Predicting the Future: AI Approaches to Time-Series Problems, and (4) Software Tools for Developing Agents.

The immense growth of the web has caused the amount of text available online to skyrocket. The AAAI-98 Workshop on Learning for Text Categorization brought together researchers from many of respective areas. A to share their different experiences four workshops were held in conjunction final panel on the synergistic effects of in tackling similar problems. Specifically, several researchers made tasks, no previous workshop soning system, what the significance the point that making use of linguistic attempted to characterize CBR integration of these synergies is, how they can be structure, as well as using stylistic and issues. This nontextual features of documents, can Workshop highlights included panel and the other discussion periods improve categorization performance.

We have a developed an analog VLSI system that models the coordination of neurobiological segmental oscillators. We have implemented and tested a system that consists of a chain of eleven pattern generating circuits that are synaptically coupled to their nearest neighbors. Each pattern generating circuit is implemented with two silicon Morris-Lecar neurons that are connected in a reciprocally inhibitory network. We discuss the mechanisms of oscillations in the two-cell network and explore system behavior based on isotropic and anisotropic coupling, and frequency gradients along the chain of oscillators.

We have a developed an analog VLSI system that models the coordination of neurobiological segmental oscillators. We have implemented and tested a system that consists of a chain of eleven pattern generating circuits that are synaptically coupled to their nearest neighbors. Each pattern generating circuit is implemented with two silicon Morris-Lecar neurons that are connected in a reciprocally inhibitory network. We discuss the mechanisms of oscillations in the two-cell network and explore system behavior based on isotropic and anisotropic coupling, and frequency gradients along the chain of oscillators.

We have a developed an analog VLSI system that models the coordination ofneurobiological segmental oscillators. We have implemented and tested a system that consists of a chain of eleven pattern generating circuits thatare synaptically coupled to their nearest neighbors. Each pattern generating circuit is implemented with two silicon Morris-Lecar neurons that are connected in a reciprocally inhibitory network. We discuss themechanisms of oscillations in the two-cell network and explore system behavior based on isotropic and anisotropic coupling, and frequency gradientsalong the chain of oscillators.