Renewed motives for space exploration have inspired NASA to work toward the goal of establishing a virtual presence in space, through heterogeneous effets of robotic explorers. Information technology, and Artificial Intelligence in particular, will play a central role in this endeavor by endowing these explorers with a form of computational intelligence that we call remote agents. In this paper we describe the Remote Agent, a specific autonomous agent architecture based on the principles of model-based programming, on-board deduction and search, and goal-directed closed-loop commanding, that takes a significant step toward enabling this future. This architecture addresses the unique characteristics of the spacecraft domain that require highly reliable autonomous operations over long periods of time with tight deadlines, resource constraints, and concurrent activity among tightly coupled subsystems. The Remote Agent integrates constraint-based temporal planning and scheduling, robust multi-threaded execution, and model-based mode identification and reconfiguration. The demonstration of the integrated system as an on-board controller for Deep Space One, NASA's rst New Millennium mission, is scheduled for a period of a week in late 1998. The development of the Remote Agent also provided the opportunity to reassess some of AI's conventional wisdom about the challenges of implementing embedded systems, tractable reasoning, and knowledge representation. We discuss these issues, and our often contrary experiences, throughout the paper.

Multi-agent domains consisting of teams of agents that need to collaborate in an adversarial environment offer challenging research opportunities. In this article, we introduce periodic team synchronization (PTS) domains as time-critical environments in which agents act autonomously with low communication, but in which they can periodically synchronize in a full-communication setting. The two main contributions of this article are a flexible team agent structure and a method for inter-agent communication in domains with unreliable, single-channel, low-bandwidth communication. First, the novel team agent structure allows agents to capture and reason about team agreements. We achieve collaboration between agents through the introduction of formations. A formation decomposes the task space defining a set of roles. Homogeneous agents can flexibly switch roles within formations, and agents can change formations dynamically, according to pre-defined triggers to be evaluated at run-time. This flexibility increases the performance of the overall team. Our teamwork structure further includes pre-planning for frequent situations. Second, the novel communication method is designed for use during the lowcommunication periods in PTS domains. It overcomes the obstacles to inter-agent communication in multi-agent environments with unreliable, high-cost, low-bandwidth communication. We fully implemented both the flexible teamwork structure and the communication method in the domain of simulated robotic soccer, and conducted controlled empirical experiments to verify their effectiveness. In addition, our simulator team made it to the semi-finals of the RoboCup-97 competition, in which 29 teams participated.

The past five years have seen dramatic advances in planning algorithms, with an emphasis on propositional methods such as Graphplan and compilers that convert planning problems into propositional CNF formulae for solution via systematic or stochastic SAT methods. Related work on the Deep Space One spacecraft control algorithms advances our understanding of interleaved planning and execution. In this survey,we explain the latest techniques and suggest areas for future research.

A new generation of reactive, model-based executives are emerging that make extensive use of componentbased declarative models to analyze anomalous situations and generate novel sequences for the internal control of complex autonomous systems. Burton, a generative, model-based planner offers a core element that bridges the gap between current and target states within the reactive loop. Burton is a sound, complete, reactive planner that generates a single control action of a valid plan in average case constant time, and compensates for anomalies at every step. Burton will not generate irreversible, potentially damaging sequences, except to effect repairs. We present model compilation, causal analysis, and online policy construction methods that are key to Burton's performance. Conventional wisdom has largely pushed deductive reasoning out of the reactive control loop for nearly a decade. However, recent search for the surprisingly elusive, hard satisfiability problem foretells a health...

Emotional reasoning can be an important contribution to auto- mated tutoring and training systems. This paper describes mile, a model of emotional reasoning that builds upon existing approaches and significantly generalizes and extends their capabilities. The main contribution is to show how an explicit planning model allows a more general treatment of several stages of the reasoning process. The model supports educational applications by allowing agents to appraise the emotional significance of events as they relate to students' (or their own) plans and goals, model and predict the emotional state of others, and alter behavior accordingly.

The Simple Temporal Network formalism permits significant flexibility in specifying the occurrence time of events in temporal plans. However, to retain this flexibility during execution, there is a need to propagate the actual execution times of past events so that the occurrence windows of future events are adjusted appropriately. Unfortunately, this may run afoul of tight real-time control requirements that dictate extreme efficiency. The performance may be improved by restricting the propagation. However, a fast, locally propagating, execution controller may incorrectly execute a consistent plan. To resolve this dilemma, we identify a class of dispatchable networks that are guaranteed to execute correctly under local propagation. We show that every consistent temporal plan can be reformulated as an equivalent dispatchable network, and we present an algorithm that constructs such a network. Moreover, the constructed network is shown to have a minimum number of edges among all such n...

Temporal plans permit significant flexibility in specifying the occurrence time of events. Plan execution can make good use of that flexibility. However, the advantage of execution flexibility is counterbalanced by the cost during execution of propagating the time of occurrence of events throughout the flexible plan. To minimize execution latency, this propagation needs to be very e#cient. Previous work showed that every temporal plan can be reformulated as a dispatchable plan, i.e., one for which propagation to immediate neighbors is su#cient. A simple algorithm was given that finds a dispatchable plan with a minimum number of edges in cubic time and quadratic space. In this paper, we focus on the e#ciency of the reformulation process, and improve on that result. A new algorithm is presented that uses linear space and has time complexity equivalent to Johnson's algorithm for all-pairs shortest-path problems. Experimental evidence confirms the practical e#ectiveness...

In this paper, we examine the motivations for research on cognitive architectures and review some candidates that have been explored in the literature. After this, we consider the capabilities that a cognitive architecture should support, some properties that it should exhibit related to representation, organization, performance, and learning, and some criteria for evaluating such architectures at the systems level. In closing, we discuss some open issues that should drive future research in this important area. Key words: cognitive architectures, intelligent systems, cognitive processes 1

Unary operator domains -- i.e., domains in which operators have a single effect -- arise naturally in many control problems. In its most general form, the problem of strips planning in unary operator domains is known to be as hard as the general strips planning problem -- both are pspace-complete. However, unary operator domains induce a natural structure, called the domain's causal graph. This graph relates between the preconditions and effect of each domain operator. Causal graphs were exploited by Williams and Nayak in order to analyze plan generation for one of the controllers in NASA's Deep-Space One spacecraft. There, they utilized the fact that when this graph is acyclic, a serialization ordering over any subgoal can be obtained quickly. In this paper we conduct a comprehensive study of the relationship between the structure of a domain's causal graph and the complexity of planning in this domain. On the positive side, we show that a non-trivial polynomial time plan generation algorithm exists for domains whose causal graph induces a polytree with a constant bound on its node indegree. On the negative side, we show that even plan existence is hard when the graph is a directed-path singly connected DAG.

The trend to increasingly capable and affordable control processors has generated an explosion of embedded real-time gadgets that serve almost every function imaginable. The daunting task of programming these gadgets is greatly alleviated with real-time deductive engines that perform all execution and monitoring functions from a single core model. Fast response times are achieved using an incremental propositional deductive database (an LTMS). Ideally the cost of an LTMS's incremental update should be linear in the number of labels that change between successive contexts. Unfortunately an LTMS can expend a significant percentage of its time working on labels that remain constant between contexts. This is caused by the LTMS's conservative approach: a context switch first removes all consequences of deleted clauses, whether or not those consequences hold in the new context. This paper presents a more aggressive incremental TMS, called the ITMS, that avoids processing a significant number...