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Drew McDermott Research on planning for robots is in such a state of flux that there is disagreement about what planning is and whether it is necessary. We can take planning to be the optimization and debugging of a robot's program by reasoning about possible courses of execution. It is necessary to the extent that fragments of robot programs are combined at run time. There are several strands of research in the field; I survey six: (1) attempts to avoid planning; (2) the design of flexible plan notations; (3) theories of time-constrained planning; (4) planning by projecting and repairing faulty plans; (5) motion planning; and (6) the learning of optimal behaviors from reinforcements. More research is needed on formal semantics for robot plans.

What are the powerful new ideas in knowledge based design? What important research issues require further investigation? Perhaps the key research problem in AIbased design for the 1980's is to develop better models of the design process. A comprehensive model of design should address the following aspects of the design process: the state of the design; the goal structure of the design process; design decisions; rationales for design decisions; control of the design process; and the role of learning in design This article presents some of the most important ideas emerging from current AI research on design, especially ideas for better models of design It is organized into sections dealing with each of the aspects of design listed above What is design? Why should we study it?

This overview of the Yale Artificial Intelligence Project serves as an introduction to Scientific Datalink's microfiche publication of Yale AI Technical Reports Researchers develop new ideas and plant them in programs. The programs are cultivated, hybridized, nurtured. The weaker ideas die out. The stronger ideas are grafted onto new stock and serve as the basis of hearty new strains. At Yale, there has been a traditional summer seminar series at which graduate students present their unprepossessing theories to the vocal and critical review of their colleagues.

Although researchers have studied planning since the early days of AI, recent developments have revolutionized the field. Furthermore, work on propositional planning is closely related to the algorithms used in the autonomous controller for the National Aeronautics and Space Administration (NASA) Deep Space One spacecraft, launched in October 1998. As a result, our understanding of interleaved planning and execution has advanced as well as the speed with which we can solve classical planning problems. The goal of this survey is to explain these recent advances and suggest new directions for research. Because this article requires minimal AI background (for example, simple logic and basic search algorithms), it's suitable for a wide audience, but my treatment is not exhaustive because I don't have the space to discuss every active topic of planning research.

This research was sponsored in part by the Office of Naval Research under contract NOOO14.80-C-0732 and the National Science Foundation under grant hZCS79-06543 IUNIX is trademark of Bell Laboratories These include the following: 1. A robust language analyzer, which almost never has a "hard" failure and which has the ability to handle most elliptical constructions in context 2 A context and memory mechanism that determines the focus of attention and helps with lexical and syntactic disambiguation, and with some aspects of pronominal reference. While some of the components of the system are experimental in nature: the basic features of UC provide a usable device to obtain information about UNIX. In addition, THE AI,MAGAZINE Spring 1984 29 it is straightforward to extend UC's knowledge base to cover UNIX with which UC is not currently familiar. How do I delete a file?

The goal of this group is to explore the use of domainspecific knowledge and natural deduction-based reasoning techniques to construct theorem provers that operate in nontrivial mathematical domains. Two new provers, by Larry IIines and Tie-Cheng Wang, are very much like expert systems, since the prover takes its direction by trying to satisfy "higher level" goals, based on knowledge about theorem proving. These are stand-alone provers, not man-machine systems, which are attacking some fairly difficult theorems in mathematics. In addition to this mainline work on mathematical theorem provers, two auxiliary efforts rely heavily on knowledge-based deduction. Michael Starbird is developing a knowledge-based expert system for an area of geometric topology, particularly for three dimensions.

In a large-scale space, structure is at a significantly larger scale than the observations available at an instant To learn the structure of a large-scale space from observations, the observer must build a cognitive map of the environment by integrating observations over an extended period of time, inferring spatial structure from perceptions and the effects of actions The cognitive map representation of largescale space must account for a mapping, or learning structure from observations, and navigation, or creating and executing a plan to travel from one place to another Approaches to date tend to be fragile either because they don't build maps; or because they assume nonlocal observations, such as those available in preexisting maps or global coordinate systems, including active Thus, to learn the large-scale structure of the space, the traveler must necessarily build a cognitive map of the environment by integrating observations over extended periods of time, inferring spatial structure from perceptions and the effects of actions. Large-scale space and the corresponding cognitive map representation cannot be defined independent of sensory perceptions or motor actions used to observe and move about in this environment For example, a work bench observed by a laser-bearing robot is not a large-scale space, but the moon is a large-scale space relative to a land-roving robot. A microchip is not large scale relative to an optical inspection system, but a grasshopper ganglion is a large-scale space when observed by an electron microscope. Inverse trigonometric operations and scalar multiplication require ratio data, in which a numeric value is calibrated with respect to a true zero. Trigonometric operations can require only interval data on angles, where differences are well defined, but absolute angles are not required.

How did TALPLANNER come about? TAL serves as a reference formalism for We use a simple gripper domain as an example. ROBBY only has a single gripper. For many domains, the process is intuitive and straightforward. We imagine that for other domains, the process will be quite complex, and finding a means of automatically generating at least some of the control statements is highly desirable and a challenging research issue.

The system consists of an overall agent architecture and five components within the architecture. The five components are (1) goaldirected learning (GDL), a decisiontheoretic method for selecting learning goals; (2) probabilistic bias evaluation (PBE), a technique for using probabilistic background knowledge to select learning biases for the learning goals; (3) uniquely predictive theories (UPTs) and probability computation using independence (PCI), a probabilistic representation and Bayesian inference method for the agent's theories; (4) a probabilistic learning component, consisting of a heuristic search algorithm and a Bayesian method for evaluating proposed theories; and (5) a decision-theoretic probabilistic planner, which searches through the probability space defined by the agent's current theory to select the best action. PAGODA's initial learning goal is just An autonomous agent must be able to select biases (Mitchell 1980) for new learning tasks as they arise. PBE uses probabilistic background knowledge and a model of the system's expected learning performance to compute the expected value of learning biases for each learning goal. The resulting expected discounted future accuracy is used as the expected value of the bias.