Planning domains often feature subproblems such as route planning and resource handling. Using static domain analysis techniques, we have been able to identify certain commonly occurring subproblems within planning domains, making it possible to abstract these subproblems from the overall goals of the planner and deploy specialized technology to handle them in a way integrated with the broader planning activities. Using two such subsolvers our hybrid planner, stan4, participated successfully in the Fifth International Conference on Artificial Intelligence Planning and Scheduling (AIPS'00) planning competition.

Fast-forward (FF) was the most successful automatic planner in the Fifth International Conference on Artificial Intelligence Planning and Scheduling (AIPS '00) planning systems competition. Like the well-known hsp system, FF relies on forward search in the state space, guided by a heuristic that estimates goal distances by ignoring delete lists. It differs from HSP in a number of important details. This article describes the algorithmic techniques used in FF in comparison to hsp and evaluates their benefits in terms of run-time and solution-length behavior.

Mips is a planning system that applies binary decision diagrams (BDDs) to compactly represent world states in a planning problem and efficiently explore the underlying state space. It was the first general planning system based on model-checking methods. At the Fifth International Conference on Artificial Intelligence Planning and Scheduling (AIPS'00), mips was one of five planning systems to be awarded for distinguished performance in the fully automated track. This article gives a brief introduction to, and explains the basic planning algorithm used by, mips, using a simple logistics problem as an example.

This article presents the GRT planner, a forward heuristic state-space planner, and comments on the results obtained from the Fifth International Conference on Artificial Intelligence Planning and Scheduling (AIPS'00) planning competition. The grt planner works in two phases. In the preprocessing phase, it estimates the distances between the facts and the goals of the problem. During the search phase, the estimates are used to guide a forward-directed search.

As the title indicates, Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence covers the design and development of multiagent and distributed AI systems. The purpose of this book is to provide a comprehensive overview of the field. It is an excellent collection of closely related papers that provides a wonderful introduction to multiagent systems and distributed AI.

Knowledge portals provide views onto domain-specific information on the World Wide Web, thus helping their users find relevant, domain-specific information. The construction of intelligent access and the contribution of information to knowledge portals, however, remained an ad hoc task, requiring extensive manual editing and maintenance by the knowledge portal providers. To diminish these efforts, we use ontologies as a conceptual backbone for providing, accessing, and structuring information in a comprehensive approach for building and maintaining knowledge portals. We present one research study and one commercial case study that show how our approach, called seal (semantic portal), is used in practice.

This book is an introduction to support vector machines and related kernel methods in supervised learning, whose task is to estimate an input-output functional relationship from a training set of examples. A learning problem is referred to as classification if its output take discrete values in a set of possible categories and regression if it has continuous real-valued output.

This article presents a learning agent shell and methodology for building knowledge bases and agents and their innovative application to the development of a critiquing agent for military courses of action, a challenge problem set by the Defense Advanced Research Projects Agency's High-Performance Knowledge Bases Program. The learning agent shell includes a general problem-solving engine and a general learning engine for a generic knowledge base structured into two main components: (1) an ontology that defines the concepts from an application domain and (2) a set of task-reduction rules expressed with these concepts. The development of the critiquing agent was done by importing ontological knowledge from cyc and teaching the agent how an expert performs the critiquing task. The learning agent shell, the methodology, and the developed critiquer were evaluated in several intensive studies, demonstrating good results.

LifeCode is a natural language processing (NLP) and expert system that extracts demographic and clinical information from free-text clinical records. The LifeCode NLP engine uses a large number of specialist readers whose particular output are combined at various levels to form an integrated picture of the patient's medical condition(s), course of treatment, and disposition. The LifeCode expert system performs the tasks of combining complementary information, deleting redundant information, assessing the level of medical risk and level of service represented in the clinical record, and producing an output that is appropriate for input to an electronic medical record (EMR) system or a hospital information system. The LifeCode NLP and expert systems reside in various delivery packages, including online transaction processing, a web browser interface, and an automated speech recognition (ASR) interface.

Machine learning, and more particularly learning with neural networks, can be viewed as just such a phenomenon. Frequently remarkable performance is obtained by training networks to perform relatively complex AI tasks. The need for a fuller theoretical analysis and understanding of their performance has been a major research objective for the last decade. Neural Network Learning: Theoretical Foundations reports on important developments that have been made toward this goal within the computational learning theory framework.