This is a summary of the Workshop on Planning that was sponsored by the Defense Advanced Research Project Agency and held in Santa Cruz, California, on October 21-23, 1987. The purpose of this workshop was to identify and explore new directions for research in planning.

This paper presents a framework for exploring issues in time-dependent planning: planning in which the time available to respond to predicted events varies, and the decision making required to formulate effective responses is complex. Our analysis of time-dependent planning suggests an approach based on a class of algorithms that we call anytime algorithms. Anytime algorithms can be interrupted at any point during computation to return a result whose utility is a function of computation time. We explore methods for solving time-dependent planning problems based on the properties of anytime algorithms. Time-dependent planning is concerned with determining how best to respond to predicted events when the time available to make such determinations varies from situation to situation.

In this paper, the reasoning and planning capabilities of an autonomous mobile robot are described; The reasoning system that controls the robot is designed to exhibit the kind of behavior expected of a rational agent, and is endowed with the psychological attitudes of belief, desire, and intention. Because these attitudes are explicitly represented, they can be manipulated and reasoned about, resulting in complex goal-directed and reflective behaviors. Unlike most planning systems, the plans or intentions formed by the robot need only be partly elaborated before it decides to act. This allows the robot to avoid overly strong expectations about the environment, overly constrained plans of action, and other forms of overcommitment common to previous planners. In addition, the robot is continuously reactive and has the ability to change its goals and intentions as situations warrant. The system has been tested with SRI's autonomous robot (Flakey) in a space station scenario involving navigation and the performance of emergency tasks. 1

Large engineering projects, such as the engineering development of computers, involve a large number of activities and require cooperation across a number of departments. Due to technological and market uncertainties, these projects involve the management of a large number of changes. The Callisto project was born out of realization that the classical approaches to project management do not provide sufficient functionally to manage large engineering projects. Callisto was initiated as a research effort to explore project scheduling, control and configuration problems during the engineering prototype development of large computer systems and to devise intelligent project management tools that facilitate the documentation of project management expertise and its reuse from one project to another. In the first phase of the project, rule-based prototypes were used to build quick prototypes of project management expertise and the project management knowledge required to support expert project managers. In the second phase, the understanding of point solutions was used to capture the underlying models of project management in distributed project negotiations and comparative analysis. This article provides an overview of the problems, experiments, and the resulting models of project knowledge and constraint-directed negotiation.

Human schedulers are typically overburdened by the complexity of this task, and conventional computer-based scheduling systems consider only a small fraction of the relevent knowledge. This article describes research aimed at providing a framework in which all relevant scheduling knowledge can be given consideration during schedule generation and revision. Factory scheduling is cast as a complex constraint-directed activity, driven by a rich symbolic model of the factory environment in which various influencing factors are formalized as constraints. Two knowledge-based factory scheduling systems that implement aspects of this approach are described.

To be useful in practice, a factory production schedule must reflect the influence of a large and conflicting set of requirements, objectives and preferences. Human schedulers are typically overburdened by the complexity of this task, and conventional computer-based scheduling systems consider only a small fraction of the relevent knowledge. This article describes research aimed at providing a framework in which all relevant scheduling knowledge can be given consideration during schedule generation and revision. Factory scheduling is cast as a complex constraint-directed activity, driven by a rich symbolic model of the factory environment in which various influencing factors are formalized as constraints. A variety of constraint-directed inference techniques are defined with respect to this model to provide a basis for intelligently compromising among conflicting concerns. Two knowledge-based factory scheduling systems that implement aspects of this approach are described.

This paper presents a unique real-time obstacle avoidance approach for manipulators and mobile robots based on the artificial potential field concept. Collision avoidance, tradi tionally considered a high level planning problem, can be effectively distributed between different levels of control, al lowing real-time robot operations in a complex environment. This method has been extended to moving obstacles by using a time-varying artificial patential field. We have applied this obstacle avoidance scheme to robot arm mechanisms and have used a new approach to the general problem of real-time manipulator control. We reformulated the manipulator con trol problem as direct control of manipulator motion in oper ational space--the space in which the task is originally described--rather than as control of the task's corresponding joint space motion obtained only after geometric and kine matic transformation.

The MITRE Corporation is a scientific and technical an acronym for Knowledge-Based System. Subsequently, organization engaged in system engineering activities, Rome Air Development Center took over support of the principally in support of the United States Air Force and project and continues to fund part of our AI research effort. MITRE is a special kind of engineering MITRE's current research is summarized below. The corporation is a Federal Contract Bedford center is supported by 15 Symbolics Lisp machines Research Center, a designation covering the handful netted to two Vax-780 file servers, while the Washington of independent institutions that perform governmentsponsored center is supported by both a classified and an unclassified research. It is an independent, nonprofit corporation facility, with 2 Lambdas and 2 Symbolics Lisp machines designed and m.anagcd to provide long-term assistance respectively netted to Vax-780 file servers.

The founding of a new college at Rutgers in 1969 became the occasion for building a strong computer science presence in the University. Livingston College thus provided the home for the newly organized Department of Computer Science (DCS) and for the beginning of computer science research at Rutgers.

O-Plan is an AI planner based on previous experience with the Nonlin planner and its derivatives. Nonlin and other similar planning systems had limited control architectures and were only partially successful at limiting their search spaces. O-Plan is a design and implementation of a more flexible system aimed at supporting planning research and development, opening up new planning methods and supporting strong search control heuristics. O-Plan takes an engineering approach to the construction of an efficient domain-independent planning system which includes a mixture of AI and numerical techniques from operations research. The main contributions of the work are centred around the control of search within the O-Plan planning framework, and this paper outlines the search control heuristics employed within the planner.