The Workshop on Comparative Analysis of AI Planning Systems, held during the 1994 national AI conference, was lively and interesting. Both the theoretical and practical sides of the AI planning community were represented. Several papers contributed to the theoretical analysis of planning algorithms, and others showed the first steps toward convergence between such theoretical work and practical work on the system engineering aspects of working planners. Both the theoretical and practical sides of the AI planning community were represented, and both sides seemed to understand the other side better after the workshop. Several papers contributed further to the theoretical analysis of planning algorithms, either through frameworks for reconstructing planning algorithms or through empirical studies (Christer Backstrom, Linkoping University, Sweden; Subbarao Kambhampati, Arizona State University; Henry Kautz, AT&T Bell Labs; Craig Knoblock, University of Southern California/Information Sciences Institute [USC/ISI]; and Qiang Yang, University of Waterloo, Canada).

We describe an approach to intelligent user interfaces, based on the idea of making the computer a collaborator, and an application-independent technology for implementing such interfaces. For us, any interface that is called intelligent should at least be able to answer the six types of questions from users shown in figure 1. Being able to ask and answer these kinds of questions implies a flexible and adaptable division of labor between the human and the computer in the interaction process. Unlike most current interfaces, an intelligent user interface should be able to guide and support you when you make a mistake or if you don't know how to use the system well. What we are suggesting here is a paradigm shift. As an analogy, consider the introduction of the undo button.

My Ph.D. dissertation (Calistri 1990) extends traditional methods of plan recognition to handle situations in which agents have flawed plans. This extension involves solving two problems: determining what sorts of mistakes people make when they reason about plans and figuring out how to recognize these mistakes when they occur. I have developed a complete classification of plan-based misconceptions, which categorizes all ways that a plan can fail, and I have developed a probabilistic interpretation of these misconceptions that can be used in principle to guide a bestfirst-search algorithm. I have also developed a program called Pathfinder that embodies a practical implementation of this theory. Pathfinder is a probability-based plan-recognition system based on the A* algorithm that uses information available from a user model to guide a bestfirst search through a plan hierarchy.

Introduction In the following two subsections, we present a brief discussion of the project management problem and how the Callisto project began. The Project Management Problem Innovation is important to the continued vitality of industry. New products and changes in existing products are occurring at an increasing rate, causing product lives to decrease. In order to maintain market share, companies are forced to reduce product development time and bring their products to the market as early as possible. A major portion of development involves performing and managing many activities. For example, in hightechnology industries such as the computer industry, thousands of activities must be performed to design and build the prototype of a new product. Poor performance or management of an activity can result in critical delays. If product development time is to be reduced, better management and technical support are crucial. The Callisto project was started at the initiative of Digital ...

The methodological underpinnings of AI are slowly changing. Benchmarks, test beds, and controlled experimentation are becoming more common. Although we are optimistic that this change can solidify the science of AI, we also recognize a set of difficult issues concerning the appropriate use of this methodology. We discuss these issues as they relate to research on agent design. We survey existing test beds for agents and argue for appropriate caution in their use.

This article provides an overview of the nature and role of autonomy for space exploration, with a bias in focus towards describing the relevance of AI technologies. It explores the range of autonomous behavior that is relevant and useful in space exploration and illustrates the range of possible behaviors by presenting four case studies in space-exploration systems, each differing from the others in the degree of autonomy exemplified. Three core requirements are defined for autonomous space systems, and the architectures for integrating capabilities into an autonomous system are described. The article concludes with a discussion of the challenges that are faced currently in developing and deploying autonomy technologies for space. As NASA and other space agencies around the world formulate and deploy missions to return to the moon and explore Mars and beyond, the realization is emerging that smarter mobile systems that are themselves instruments of knowledge and understanding must be ...

A principal one among them is the existence of multiple domains that share the same underlying causal structure for actions. We describe an approach that exploits this shared causal structure to discover a hierarchical task structure in a source domain, which in turn speeds up learning of task execution knowledge in a new target domain. Our approach is theoretically justified and compares favorably to manually designed task hierarchies in learning efficiency in the target domain. We demonstrate that causally motivated task hierarchies transfer more robustly than other kinds of detailed knowledge that depend on the idiosyncrasies of the source domain and are hence less transferable. These domains are complex, and good performance requires selecting long chains of actions to achieve subgoals needed for ultimate success.

The DSE provides core automation functionality for scheduling the network, including the interpretation of scheduling requirements expressed by users, their elaboration into tracking passes, and the resolution of conflicts and constraint violations. The DSE incorporates both systematic search-and repairbased algorithms, used for different phases and purposes in the overall system. It has been integrated with a web application that provides DSE functionality to all DSN users through a standard web browser, as part of a peer-to-peer schedule negotiation process for the entire network. The system has been deployed operationally and is in routine use, and is in the process of being extended to support long-range planning and forecasting and near real-time scheduling. ASA's Deep Space Network (DSN) provides communications and other services for planetary exploration missions as well as other missions beyond geostationary orbit, supporting both NASA and international users.

Assembly plays a fundamental role in the manufacturing of most products. Parts that have been individually formed or machined to meet designed specifications are assembled into a configuration that achieves the functions of the final product or mechanism. The economic importance of assembly as a manufacturing process has led to extensive efforts to improve the efficiency and cost effectiveness of assembly operations. The sequence of mating operations that can be carried out to assemble a group of parts is constrained by the geometric and mechanical properties of the parts, their assembled configuration, and the stability of the resulting subassemblies. An approach to representation and reasoning about these sequences is described here and leads to several alternative explicit and implicit plan representations.

This path, however, is typically not a shortest path in the continuous terrain. In this overview article, we discuss a path-planning methodology for quickly finding paths in continuous terrain that are typically shorter than shortest grid paths. Anyangle path-planning algorithms are variants of the heuristic path-planning algorithm A* that find short paths by propagating information along grid edges (like A*, to be fast) without constraining the resulting paths to grid edges (unlike A*, to find short paths). In robotics and video games, (continuous) terrain is often discretized into grids with blocked and unblocked grid cells and from there into grid graphs (Tozour 2004; Rabin 2000; Chrpa and Komenda 2011; Björnsson et al. 2003; Nash 2012). Our objective is to find short unblocked paths from given start vertices to given goal vertices.