The decision-making process of computer-controlled opponents in video games is called game AI. Adaptive game AI can improve the entertainment value of games by allowing computer-controlled opponents to ix weaknesses automatically in the game AI and to respond to changes in human-player tactics. Dynamic scripting is a reinforcement learning approach to adaptive game AI that learns, during gameplay, which game tactics an opponent should select to play effectively. In previous work, the tactics used by dynamic scripting were designed manually. We introduce the evolutionary state-based tactics generator (ESTG), which uses an evolutionary algorithm to generate tactics automatically. Experimental results show that ESTG improves dynamic scripting's performance in a real-time strategy game. We conclude that high-quality domain knowledge can be automatically generated for strong adaptive game AI opponents. Game developers can bene it from applying ESTG, as it considerably reduces the time and effort needed to create adaptive game AI.

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.

The 1994 Workshop on Case-Based Reasoning (CBR) focused on the evaluation of CBR theories, models, systems, and system components. The CBR community addressed the evaluation of theories and implemented systems, with the consensus that a balance between novel innovations and evaluations could maximize progress.

The Association for the Advancement of Artificial Intelligence (AAAI) held its 1995 Fall Symposia Series on 10 to 12 November in Cambridge, Massachusetts. This article contains summaries of the eight symposia that were conducted: (1) Active Learning; (2) Adaptation of Knowledge for Reuse; (3) AI Applications in Knowledge Navigation and Retrieval; (4) Computational Models for Integrating Language and Vision; (5) Embodied Language and Action Symposium; (6) Formalizing Context; (7) Genetic Programming; and (8) Rational Agency: Concepts, Theories, Models, and Applications.

The 1994 Workshop on Case-Based Reasoning (CBR) focused on the evaluation of CBR theories, models, systems, and system components. The CBR community addressed the evaluation of theories and implemented systems, with the consensus that a balance between novel innovations and evaluations could maximize progress.

The Association for the Advancement of Artificial Intelligence (AAAI) held its 1995 Fall Symposia Series on 10 to 12 November in Cambridge, Massachusetts. This article contains summaries of the eight symposia that were conducted: (1) Active Learning; (2) Adaptation of Knowledge for Reuse; (3) AI Applications in Knowledge Navigation and Retrieval; (4) Computational Models for Integrating Language and Vision; (5) Embodied Language and Action Symposium; (6) Formalizing Context; (7) Genetic Programming; and (8) Rational Agency: Concepts, Theories, Models, and Applications.