If classical planners are ever to automatically plan the actions of the smart machines, particularly robots for the automatic assembly of industrial objects, then they will have to know much more about geometry and topology as well as sensing. Consider that the simple act of changing an object's grasp -- the change might be necessitated by the nature of some assembly goal -- involves the interaction of the geometries of the grasping device and the object if the change is to occur without a collision between the device and the object. Of course, one could ask, Could geometric considerations be divorced from the highly developed symbolic-level planning? That is, could we first synthesize a symbolic plan and then plug in the geometry for the execution of the actions? Experience has shown the answer to, unfortunately, be a big no.

In this article, we present Spar (simultaneous planner for assembly robots), an implemented system that reasons about high-level operational goals, geometric goals, and uncertainty-reduction goals to create task plans for an assembly robot. These plans contain manipulations to achieve the assembly goals and sensory operations to cope with uncertainties in the robot's environment. High-level goals (which we refer to as operational goals) are satisfied by adding operations to the plan using a nonlinear, constraint-posting method. Geometric goals are satisfied by placing constraints on the execution of these operations. If the geometric configuration of the world prevents this, Spar adds new operations to the plan along with the necessary set of constraints on the execution of these operations. When the uncertainty in the world description exceeds that specified by the uncertainty-reduction goals, Spar introduces either sensing operations or manipulations to reduce this uncertainty to acceptable levels. If Spar cannot find a way to sufficiently reduce uncertainties, it augments the plan with sensing operations to be used to verify the execution of the action and, when possible, posts possible error-recovery plans, although at this point, the verification operations and recovery plans are predefined.

Many barriers exist today that prevent effective industrial exploitation of current and future AI research. These barriers can only be removed by people who are working at the scientific forefront in AI and know potential industrial needs. The Knowledge Processing Laboratory's research and development concentrates in the following areas: (1) natural language interfaces to knowledge-based systems and databases; (2) theoretical and experimental work on qualitative modeling and nonmonotonic reasoning for future knowledge-based systems; (3) application-specific language design, in particular, Prolog extensions; and (4) desi gn and analysis of neural networks. This article gives the reader an overview of the main topics currently being pursued in each of these areas.

Stewart Brand, of Whole Earth Catalog fame, is a technology enthusiast. In 1986, he spent three months in the fantasyland of his choice, MIT's Media Laboratory (formerly the Architecture Machine Group). In his latest book, The Media Lab: Inventing the Future at MIT (Viking/ Penguin, New York, 1988, 285 pp., $10, ISBN 0-14-009701-5), he tells the world what he found.

To be considered exceptional, a textbook must satisfy three basic requirements. First, it must be authoritative, written by one with a broad range of experience in, and knowledge of, a subject. Second, it must effectively communicate to the reader, in the same manner in which a course instructor must be capable of imparting knowledge to students in a classroom. Third, it must stimulate the reader into thinking more deeply about the subject and into viewing it from fresh perspectives. In Artificial Intelligence: A Knowledge-Based Approach (Boyd and Fraser, Boston, 740 pp., $48.95), author Morris W. Firebaugh has succeeded in meeting each of these requirements.

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. The Pleiades system will provide an interactive software environment for designers to evaluate alternative systems and product designs through their impact on the feasibility and complexity of the resulting assembly sequences.

AAAI-90.

Proc. AAAI-90.

In Expert Planning Systems, pp. 245–250. Institute of Electrical Engineers.

"We describe a robot control architecture which combines a stimulus-response subsystem for rapid reaction, with a search-based planner for handling unanticipated situations. The robot agent continually chooses which action it is to perform, using the stimulusresponse subsystem when possible, and falling back on the planning subsystem when necessary. Whenever it is forced to plan, it applies an explanation-based learning mechanism to formulate a new stimulus-response rule to cover this new situation and others similar to it. With experience, the agent becomes increasingly reactive as its learning component acquires new stimulus-response rules that eliminate the need for planning in similar subsequent situations. This Theo-Agent architecture is described, and results are presented demonstrating its ability to reduce routine reaction time for a simple mobile robot from minutes to under a second."In AAAI-90, Vol. 2, pp. 1051– 1058