It is no pure coincidence that an organic chemist rather than a computer specialist is writing this foreword.

Harold Shephard Samuel D. Thurman William T. Lake JOINDER OF CLAIMS, COUNTERCLAIMS, AND CROSS-COMPLAINTS: SUGGESTED REVISION OF THE CALIFORNIA PROVISIONS. Research in artificial intelligence, a branch of computer science, has illuminated our capacity to use computers to model human thought processes. In this Article we will argue that the time has come for serious interdisciplinary work between lawyers and computer scientists to explore the computer's potential in law. Interdisciplinary work between the lawyer and the computer scientist has floundered on the misconceptions that each has of the other's discipline. As a result, no one has yet attempted computer programs incorporating complex techniques of legal reasoning. Even efforts in legal information retrieval have been hampered by these misconceptions. In retrieval, lawyers have viewed the computer as, at most, a storehouse from which cases and statutes might be retrieved by skillfully designed indexing systems.

The nature of the business doesn't matter; in every business computers have made numerous changes in record keeping, process control, and decision-making. And there will be more. One of the most important trends in computing is making computers behave intelligently. The software underneath this intelligent behavior is called an expert system, sometimes also called a knowledgebased system, or knowledge system. An expert system is a computer program that reasons about a problem in much the same way, and with about the same performance, as specialists. This chapter is about the trend toward using expert systems: what it means, how it's possible, and how to think about it. There have been lead articles about this in Fortune, Business Week, and Newsweek; most Fortune-SOO companies are using expert systems; many are establishing research and development groups for them; even staid IBM is marketing expert systems tools and using them internally. Bruce G. Buchanan I 129 There are many reasons why companies want to build an expert system. Most of them are based on the premise that: Expertise is a scarce resource. And the corollary (by Murphy's Law): Even when there is enough expertise, it is never close enough to the person who needs it in a hurry. Because this is true, almost by definition, an expert system containing some of the knowledge of a company's specialists may have several benefits.. There are several examples of expert systems working in various problem areas. At present, they are used more as "intelligent assistants" than as replacements for technicians or experts. That is, they help people think through difficult problems and may provide suggestions about what to do, without taking over every aspect of the task. Although the problems are quite different they can be categorized into two major classes problems of interpretation and problems of construction. Interpretive problem examples include Schlumberger's Dipmeter Advisor, which replicates the expertise of some of their company-wide specialists who interpret data from clients' oil wells and then sell the expert system's interpretations around the world.

Teams of artificially intelligent planetary rovers have tremendous potential for space exploration, allowing for reduced cost, increased flexibility and increased reliability. However, having these multiple autonomous devices acting simultaneously leads to a problem of coordination: to achieve the best results, the they should work together. Due to the large distances and harsh environments, a rover must be able to perform a wide variety of tasks with a wide variety of potential teammates in uncertain and unsafe environments. Instead, this article examines tackling this problem through the use of coordinated reinforcement learning: rather than being programmed what to do, the rovers iteratively learn through trial and error to take take actions that lead to high overall system return.

Intelligent systems that learn interactively from their end-users are quickly becoming widespread. Until recently, this progress has been fueled mostly by advances in machine learning; however, more and more researchers are realizing the importance of studying users of these systems. We present a number of case studies that characterize the impact of interactivity, demonstrate ways in which some existing systems fail to account for the user, and explore new ways for learning systems to interact with their users. We argue that the design process for interactive machine learning systems should involve users at all stages: explorations that reveal human interaction patterns and inspire novel interaction methods, as well as refinement stages to tune details of the interface and choose among alternatives.

This article describes the DSN scheduling wngine (DSE) component of a new scheduling system being deployed for NASA's deep space network. 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. It has been integrated with a web application which 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.

The challenging timeline for DARPA's Orbital Express mission demanded a flexible, responsive, and (above all) safe approach to mission planning. Mission planning for space is challenging because of the mixture of goals and constraints. These technologies had a significant impact on the success of the Orbital Express mission. Finally, we formulated a technique for converting procedural information to declarative information by transforming procedures into models of hierarchical task networks (HTNs).

This literature review covers AI techniques used for real-time strategy video games, focusing specifically on StarCraft. It finds that the main areas of current academic research are in tactical and strategic decision-making, plan recognition, and learning, and it outlines the research contributions in each of these areas. The paper then contrasts the use of game AI in academia and industry, finding the academic research heavily focused on creating game-winning agents, while the indus- try aims to maximise player enjoyment. It finds the industry adoption of academic research is low because it is either in- applicable or too time-consuming and risky to implement in a new game, which highlights an area for potential investi- gation: bridging the gap between academia and industry.

This, coupled with limited bandwidth and latencies, motivates onboard autonomy that ensures the quality of the science data return. Increasing quality of the data involves better sample selection, data validation, and data reduction. Robotic studies in Mars-like desert terrain have advanced autonomy for long distance exploration and seeded technologies for planetary rover missions. Specific capabilities include instrument calibration, visual targeting of selected features, an onboard database of collected data, and a long range path planner that guides the robot using analysis of current surface and prior satellite data.

By restructuring the course into a format that was roughly half lecture and half small-group problem-solving, I was able to significantly increase student engagement, their understanding and retention of difficult concepts, and my own enjoyment in teaching the class.