Moravec's estimates of animal equivalence jostling of the atoms in a Moravec's strengths--his insightful are based solely on hardware rock can be seen as the operation of a complete, self-aware mind data analysis, extrapolation of technology complexity. It is often the case that (after Evert) (Everett, H., Many-to extreme conclusions, and hardware alone cannot deliver performance, Worlds of Interpretation/ Quantum provocative predictions--are all here but it also requires software Mechanics, Princeton University and will probably gain him some new sufficient to the task.

Planning under uncertainty is a central problem in the study of automated sequential decision making, and has been addressed by researchers in many different fields, including AI planning, decision analysis, operations research, control theory and economics. While the assumptions and perspectives adopted in these areas often differ in substantial ways, many planning problems of interest to researchers in these fields can be modeled as Markov decision processes (MDPs) and analyzed using the techniques of decision theory. This paper presents an overview and synthesis of MDP-related methods, showing how they provide a unifying framework for modeling many classes of planning problems studied in AI. It also describes structural properties of MDPs that, when exhibited by particular classes of problems, can be exploited in the construction of optimal or approximately optimal policies or plans. Planning problems commonly possess structure in the reward and value functions used to describe performance criteria, in the functions used to describe state transitions and observations, and in the relationships among features used to describe states, actions, rewards, and observations. Specialized representations, and algorithms employing these representations, can achieve computational leverage by exploiting these various forms of structure. Certain AI techniques -- in particular those based on the use of structured, intensional representations -- can be viewed in this way. This paper surveys several types of representations for both classical and decision-theoretic planning problems, and planning algorithms that exploit these representations in a number of different ways to ease the computational burden of constructing policies or plans. It focuses primarily on abstraction, aggregation and decomposition techniques based on AI-style representations.

The past five years have seen dramatic advances in planning algorithms, with an emphasis on propositional methods such as GRAPHPLAN and compilers that convert planning problems into propositional conjunctive normal form formulas for solution using systematic or stochastic SAT methods. In this survey, I explain the latest techniques and suggest areas for future research.

The recent advances in computer speed and algorithms for probabilistic inference have led to a resurgence of work on planning under uncertainty. The aim is to design AI planners for environments where there might be incomplete or faulty information, where actions might not always have the same results, and where there might be trade-offs between the different possible outcomes of a plan. Addressing uncertainty in AI, planning algorithms will greatly increase the range of potential applications, but there is plenty of work to be done before we see practical decision-theoretic planning systems. This article outlines some of the challenges that need to be overcome and surveys some of the recent work in the area.

This article provides an overview of the field of qualitative decision theory: its motivating tasks and issues, its antecedents, and its prospects. Qualitative decision theory studies qualitative approaches to problems of decision making and their sound and effective reconciliation and integration with quantitative approaches. Although it inherits from a long tradition, the field offers a new focus on a number of important unanswered questions of common concern to AI, economics, law, psychology, and management.

The past five years have seen dramatic advances in planning algorithms, with an emphasis on propositional methods such as GRAPHPLAN and compilers that convert planning problems into propositional conjunctive normal form formulas for solution using systematic or stochastic SAT methods. Related work, in the context of spacecraft control, advances our understanding of interleaved planning and execution. In this survey, I explain the latest techniques and suggest areas for future research.

We describe an application of a dynamic replanning technique in a highly dynamic and complex domain: the military aeromedical evacuation of patients to medical treatment facilities. U.S. Transportation Command (USTRANSCOM) is the U.S. Department of Defense (DoD) agency responsible for evacuating patients during wartime and peace. Doctrinally, patients requiring extended treatment must be evacuated by air to a suitable medical treatment facility. The Persian Gulf War was the first significant armed conflict in which this concept was put to a serious test. The results were far from satisfactory -- about 60 percent of the patients ended up at the wrong destinations. In early 1993, the DoD tasked USTRANSCOM to consolidate the command and control of medical regulation and aeromedical evacuation operations. The ensuing analysis led to TRAC2ES (TRANSCOM regulating and command and control evacuation system), a decision support system for planning and scheduling medical evacuation operations. Probably the most challenging aspect of the problem has to do with the dynamics of a domain in which requirements and constraints continuously change over time. Continuous dynamic replanning is a key capability of TRAC2ES. This article describes the application and the AI approach we took in providing this capability.

STAN is a Graphplan-based planner, so-called because it uses a variety of STate ANalysis techniques to enhance its performance. STAN competed in the AIPS-98 planning competition where it compared well with the other competitors in terms of speed, finding solutions fastest to many of the problems posed. Although the domain analysis techniques STAN exploits are an important factor in its overall performance, we believe that the speed at which STAN solved the competition problems is largely due to the implementation of its plan graph. The implementation is based on two insights: that many of the graph construction operations can be implemented as bit-level logical operations on bit vectors, and that the graph should not be explicitly constructed beyond the fix point. This paper describes the implementation of STAN's plan graph and provides experimental results which demonstrate the circumstances under which advantages can be obtained from using this implementation.

Here scheduling, where we have fast, heuristic we identify a few properties of a real task and scheduling algorithms that yield dramatic produce mathematical abstractions of these speedups over traditional methods; decision properties. Again, both of those steps are perfectly making, where we have expert systems as standard good as initial exploration. But then we tools in many companies and products; work with the mathematical abstractions and and financial forecasting, where we don't hear never come back to the issues that came up in much about what people are doing, but Wall the original task. In some cases, new subfields Street firms seem to hire AI researchers at a of research have arisen based solely on this level rapid rate. of abstraction, and work becomes farther On the perception side, robots with vision and farther removed from the original motivating are revolutionizing manufacturing.

This article introduces a generic framework for constraint-directed search. The research literature in constraint-directed scheduling is placed within the framework both to provide insight into, and examples of, the framework and to allow a new perspective on the scheduling literature. We show how a number of algorithms from the constraint-directed scheduling research can be conceptualized within the framework. This conceptualization allows us to identify and compare variations of components of our framework and provides new perspective on open research issues. We discuss the prospects for an overall comparison of scheduling strategies and show that firm conclusions vis-a-vis such a comparison are not supported by the literature. Our principal conclusion is the need for an empirical model of both the characteristics of scheduling problems and the solution techniques themselves. Our framework is offered as a tool for the development of such an understanding of constraint-directed scheduling and, more generally, constraint-directed search.