We are interested in solving real-world planning problems and, to that end, argue for the use of domain knowledge in planning. We believe that the field must develop methods capable of using rich knowledge models to make planning tools useful for complex problems. In particular, we compare knowledge rich approaches such as hierarchical task network planning to minimal-knowledge methods such as STRIPS-based planners and disjunctive planners. Finally, we draw an analogy from the current focus of the planning community on disjunctive planners to the experiences of the machine learning community over the past decade.

The AAAI-2000 Workshop Program was held Sunday and Monday, 3031 July 2000 at the Hyatt Regency Austin and the Austin Convention Center in Austin, Texas. The 15 workshops held were (1) Agent-Oriented Information Systems, (2) Artificial Intelligence and Music, (3) Artificial Intelligence and Web Search, (4) Constraints and AI Planning, (5) Integration of AI and OR: Techniques for Combinatorial Optimization, (6) Intelligent Lessons Learned Systems, (7) Knowledge-Based Electronic Markets, (8) Learning from Imbalanced Data Sets, (9) Learning Statistical Models from Rela-tional Data, (10) Leveraging Probability and Uncertainty in Computation, (11) Mobile Robotic Competition and Exhibition, (12) New Research Problems for Machine Learning, (13) Parallel and Distributed Search for Reasoning, (14) Representational Issues for Real-World Planning Systems, and (15) Spatial and Temporal Granularity.

LBD is a curriculum consisting of prescribed exercises that teach children real-world skills by ciently, and replan after device faults having them perform several activities Conference of the Florida caused the original plan to become that are familiar to them. The cochairs of about the computer's role in the current The conference also had two panel the conference were Avelino Gonzalez, revolution in cognitive science. The first focused on modern University of Central Florida, and His talk came from a historical perspective--how trends in funding opportunities Massood Towhidnejad, Embry-Riddle humankind has always for AI, moderated by Ingrid Russell of Aeronautical University. The program felt an overwhelming need to understand the University of Hartford. This group chairs were Bill Manaris and Jim the world around us and to control included an impressive list of panelists: Etheredge, both of the University of it for our own benefit.

We are interested in solving real-world planning problems and, to that end, argue for the use of domain knowledge in planning. We believe that the field must develop methods capable of using rich knowledge models to make planning tools useful for complex problems. We discuss the suitability of current planning paradigms for solving these problems. In particular, we compare knowledge rich approaches such as hierarchical task network planning to minimal-knowledge methods such as STRIPS-based planners and disjunctive planners. We argue that the former methods have advantages such as scalability, expressiveness, continuous plan modification during execution, and the ability to interact with humans. However, these planners also have limitations, such as requiring complete domain models and failing to model uncertainty, that often make them inadequate for real-world problems. In this article, we define the terms knowledge-based and primitive-action planning and argue for the use of knowledge-based planning as a paradigm for solving real-world problems. We next summarize some of the characteristics of real-world problems that we are interested in addressing. Several current real-world planning applications are described, focusing on the ways in which knowledge is brought to bear on the planning problem. We describe some existing knowledge-based approaches and then discuss additional capabilities, beyond those available in existing systems, that are needed. Finally, we draw an analogy from the current focus of the planning community on disjunctive planners to the experiences of the machine learning community over the past decade.

By user-driven modeling for end users and near-optimal knowledge-driven scheduling acquired from human experts, races can produce parking schedules for about 400 daily flights in approximately 20 seconds; human experts normally take 4 to 5 hours to do the same. Scheduling results in the form of Gantt charts produced by races are also accepted by the domain experts. After daily scheduling is completed, the messages for aircraft change, and delay messages are reflected and updated into the schedule according to the knowledge of the domain experts. By analyzing the knowledge model of the domain expert, the reactive scheduling steps are effectively represented as the rules, and the scenarios of the graphic user interfaces are designed.

Enabling organizations to capture, share, and apply the collective experience and know-how of their people is seen as fundamental to competing in the knowledge economy. As a result, there has been a wave of enthusiasm and activity centered on knowledge management. To make progress in this area, issues of technology, process, people, and content must be addressed. In this article, we develop a road map for knowledge management. It begins with an assessment of the current state of the practice, using examples drawn from our experience at Schlumberger. It then sketches the possible evolution of technology and practice over a 10-year period. Along the way, we highlight ways in which AI technology, present and future, can be applied in knowledge management systems.

Carnegie Mellon University and then continued investigating issues in representation and reasoning as part of his research career for the last decade and a half. However, the engineers, as is their wont, have their own take and emphasis many faces: Its philosophical progress, instigated by the focus on on AI issues. Teaching engineering and animals, and its mathematical list gives some idea about how students interested in AI, especially face to formulating and analyzing concerns with application bring advances when they are taking courses along classes of algorithms that appear to be in theory, as has happened earlier with computer science students, presents effective in providing computers with in mathematics and physics. Many academic researchers have the difference in background and interest. For several decades, there has found that AI often elicits greater interest Also, when ideas are presented been another face to the field, a technological from fellow academics in engineering somewhat abstractly, the engineering one that provides tools for departments--many computer students might need to do extra work solving practical problems in various science departments are housed in in seeing how they might be applied domains. AI It would thus be great if there interaction with AI.

Finally, during knowledge refinement, the models are validated through simulation on paper or with prototyping, and the knowledge bases medicine, car troubleshooting, software are refined. The last of the book's authors domain-specific knowledge, and corrections or extensions to the products has been involved in this effort since standardizing the design and development of earlier ones. Thus, the book of expert systems then became The book is intended for practitioners is particularly interesting to those who the major research problems of the in knowledge management. The have been following their work. KADS methodology, as assets have become commonplace.

In this project, we have developed the ramp activity coordination expert system (races) to solve aircraft-parking problems. races includes a knowledge-based scheduling system that assigns all daily arriving and departing flights to the gates and remote spots with domain-specific knowledge and heuristics acquired from human experts. races processes complex scheduling problems such as dynamic interrelations among the characteristics of remote spots-gates and aircraft with various other constraints, for example, customs and ground-handling factors, at an airport. By user-driven modeling for end users and near-optimal knowledge-driven scheduling acquired from human experts, races can produce parking schedules for about 400 daily flights in approximately 20 seconds; human experts normally take 4 to 5 hours to do the same. Scheduling results in the form of Gantt charts produced by races are also accepted by the domain experts. races is also designed to deal with the partial adjustment of the schedule when unexpected events occur. After daily scheduling is completed, the messages for aircraft change, and delay messages are reflected and updated into the schedule according to the knowledge of the domain experts. By analyzing the knowledge model of the domain expert, the reactive scheduling steps are effectively represented as the rules, and the scenarios of the graphic user interfaces are designed. Because the modification of the aircraft dispositions, such as aircraft changes and cancellations of flights, is reflected in the current schedule, the modification should be sent to races from the mainframe for the reactive scheduling. The adjustments of the schedule are made semiautomatically by races because there are many irregularities in dealing with the partial rescheduling.

I was asked to give a visionary talk about the future applications of Artificial Intelligence technology; but I should warn you that I'm actually not very good as a visionary. Most of my predictions about what will happen in the industry don't come true even though they ought to. So I'm not going to tell you what the future holds; what I will do is to point out some of the technological trends that are at work. The outline of the talk is as follows: I'll start off by looking at the previous IAAI conferences and reflect on what we've learned from them. Then I'll look at what's changing in the hardware base that sets the context for all the computer applications we do. I think that will lead to interesting new viewpoints. Next I'll sketch what applications might arise from this new viewpoint. Finally, I'll discuss how the development of practical applications ought to interact with the scientific enterprise of trying to understand intelligence, in particular, human intelligence.