In this article, I discuss the emerging field of artificial intelligence and legal reasoning and review the new book by Anne v.d.L. Gardner, An Artificial Intelligence Approach to Legal Reasoning, published by Bradford/MIT Press (1987, 225 pp., $22.50) as the first book in its new series on the subject.

The following is a synopsis of the findings of the first AAAI Workshop on AI Applications to Battle Management held at the University of Washington, 16 July 1987. The workshop organizer, Pete Bonasso, sent a point paper to a number of invited presenters giving his opinion of what AI could and could not do for battle management. This paper served as a focus for the workshop presentations and discussions and was augmented by the workshop presentations; it can also serve as a roadmap of topics for future workshops. AI can provide battle management with such capabilities as sensor data fusion and adaptive simulations. Also, several key needs in battle management will be AI research topics for years to come, such as understanding free text and inferencing in real time. Finally, there are several areas -- cooperating systems and terrain reasoning, for example -- where, given some impetus, AI might be able to provide help in the near future.

New Hitech Computer Chess Success

Carnegie-Mellon University's Hitech chess computer scored 5-1 in the National Open Chess Championships held in Chicago March 18-20. The Championship Section in which Hitech competed, had 380 entries. Carnegie-Mellon University's Hitech chess computer scored 5-1 in the National Open Chess Championships held in Chicago March 18-20. The Championship Section in which Hitech competed, had 380 entries.

A numeric representation of uncertain and incomplete sensor knowledge called certainty grids was used successfully in several recent mobile robot control programs developed at the Carnegie-Mellon University Mobile Robot Laboratory (MRL). The certainty grid representation will allow this map to be incrementally updated in a uniform way based on information coming from various sources, including sonar, stereo vision, proximity, and contact sensors. The map will be used by planning programs to choose clear paths, identify locations (by correlating maps), identify well-known and insufficiently sensed terrain, and perhaps identify objects by shape. The certainty grid representation can be extended in the time dimension and used to detect and track moving objects.

This is Part 2 of a two-part article and discusses issues of tort liability and the use of computers in the courtroom. Part 1 of this article, which appeared in the Spring 1988 issue of AI Magazine, discussed steps that developers of AI systems can take to protect their efforts, and the attendant legal ambiguities that must eventually be addressed in order to clarify the scope of such protection. Part 2 explores the prospect of AI systems as subjects of litigation.

Sometimes less ambiguous, are extremely complex.

There was a six-way tie were losses to International Master's, for first with 5.5 points between: and there were two draws against International Grandmaster Mikhail players rated over 2500, (one an International Tal (a former world champion), International Master). Grandmaster Sergey Kudrin, From previous tournaments rated FIDE Master Michael Brooks, International by FIDE, the international chess federation, Master James Rizzitano, International Hitech has achieved a performance Master Calvin Blocker, and worthy of a FIDE rating. However, International Grandmaster Leonid at present FIDE is declining to Shamkovich. Hitech has met every points were: National Master Hitech, qualification but one for achieving a International Grandmaster Maxim rating--it is not a human. If Hitech Dlugy, International Grandmaster were eligible for a rating, its FIDE rating Walter Browne, International Grandmaster would be 2350, which would qualify Arthur Bisguier, and nine others.

It was felt that the AIM Szolovits responding, and "Uncertainty approaches, augmenting explanations

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 large-scale 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 landmark beacons and geo-locating satellites. We propose that robust navigation and mapping systems for large-scale space can be developed by adhering to a natural, four-level semantic hierarchy of descriptions for representation, planning, and execution of plans in large-scale space. The four levels are sensorimotor interaction, procedural behaviors, topological mapping, and metric mapping. Effective systems represent the environment, relative to sensors, at all four levels and formulate robust system behavior by moving flexibly between representational levels at run time. We demonstrate our claims in three implemented models: Tour, the Qualnav system simulator, and the NX robot.