Architecture for spacecraft operations planning

A system which generates plans for the dynamic environment of space operations is discussed. This system synthesizes plans by combining known operations under a set of physical, functional, and temperal constraints from various plan entities, which are modeled independently but combine in a flexible manner to suit dynamic planning needs. This independence allows the generation of a single plan source which can be compiled and applied to a variety of agents. The architecture blends elements of temperal logic, nonlinear planning, and object oriented constraint modeling to achieve its flexibility. This system was applied to the domain of the Intravehicular Activity (IVA) maintenance and repair aboard Space Station Freedom testbed

Agent independent task planning

Agent-Independent Planning is a technique that allows the construction of activity plans without regard to the agent that will perform them. Once generated, a plan is then validated and translated into instructions for a particular agent, whether a robot, crewmember, or software-based control system. Because Space Station Freedom (SSF) is planned for orbital operations for approximately thirty years, it will almost certainly experience numerous enhancements and upgrades, including upgrades in robotic manipulators. Agent-Independent Planning provides the capability to construct plans for SSF operations, independent of specific robotic systems, by combining techniques of object oriented modeling, nonlinear planning and temporal logic. Since a plan is validated using the physical and functional models of a particular agent, new robotic systems can be developed and integrated with existing operations in a robust manner. This technique also provides the capability to generate plans for crewmembers with varying skill levels, and later apply these same plans to more sophisticated robotic manipulators made available by evolutions in technology

The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

Modelling process knowledge in architectural design: A case-based approach

The paper presents on-going research aimed at the understanding and support of process knowledge in architectural design, from early and not sufficiently defined, to satisfactorily-defined phases. Today, technical, planning, management and environmental issues have created a scenario of such complexity that traditionally efficient control tools (e.g. technical manuals) are inadequate and there is a demand for new, integrated instruments to handle the decision process underlying architectural design. We assume design as a recursive and incrementally specified intentional planning activity, involving goals, constraints and their relationships. The essence of architectural design is thus encapsulated in the continual recursive transformation of the initial model, in order to map the desired state onto the enacted one. On the basis of this concept of design we describe the model of an environment aimed at progressively representing the enlarging space of acquired knowledge, and at supporting the designer's central role in the management of complexity

Mechanisms for Automated Negotiation in State Oriented Domains

This paper lays part of the groundwork for a domain theory of negotiation, that is, a way of classifying interactions so that it is clear, given a domain, which negotiation mechanisms and strategies are appropriate. We define State Oriented Domains, a general category of interaction. Necessary and sufficient conditions for cooperation are outlined. We use the notion of worth in an altered definition of utility, thus enabling agreements in a wider class of joint-goal reachable situations. An approach is offered for conflict resolution, and it is shown that even in a conflict situation, partial cooperative steps can be taken by interacting agents (that is, agents in fundamental conflict might still agree to cooperate up to a certain point). A Unified Negotiation Protocol (UNP) is developed that can be used in all types of encounters. It is shown that in certain borderline cooperative situations, a partial cooperative agreement (i.e., one that does not achieve all agents' goals) might be preferred by all agents, even though there exists a rational agreement that would achieve all their goals. Finally, we analyze cases where agents have incomplete information on the goals and worth of other agents. First we consider the case where agents' goals are private information, and we analyze what goal declaration strategies the agents might adopt to increase their utility. Then, we consider the situation where the agents' goals (and therefore stand-alone costs) are common knowledge, but the worth they attach to their goals is private information. We introduce two mechanisms, one 'strict', the other 'tolerant', and analyze their affects on the stability and efficiency of negotiation outcomes.Comment: See http://www.jair.org/ for any accompanying file

Human Management of the Hierarchical System for the Control of Multiple Mobile Robots

In order to take advantage of autonomous robotic systems, and yet ensure successful completion of all feasible tasks, we propose a mediation hierarchy in which an operator can interact at all system levels. Robotic systems are not robust in handling un-modeled events. Reactive behaviors may be able to guide the robot back into a modeled state and to continue. Reasoning systems may simply fail. Once a system has failed it is difficult to re-start the task from the failed state. Rather, the rule base is revised, programs altered, and the task re-tried from the beginning