Karen L. Myers

The Saphira architecture: a design for autonomy

Mobile robots, if they are to perform useful tasks and become accepted in open environments, must be fully autonomous. Autonomy has many different aspects ; here the focus is on three central ones: the ability to attend to another agent, to take advice about the environment, and to carry out assigned tasks. All three involve complex sensing and planning operations on the part of the robot, including the use of visual tracking of humans, coordination of motor controls, and planning. It is shown how these capabilities are integrated in the Saphira architecture, using the concepts of coordination of behaviour, coherence of modelling, and communication with other agents.

SRI International FASTUS system: MUC-6 test results and analysis

SRI International participated in the MUC-6 evaluation using the latest version of SRIs FASTUS system [1]. The FASTUS system was originally developed for participation in the MUC-4 evaluation [3] in 1992, and the performance of FASTUS in MUC-4 helped demonstrate the viability of finite state technologies in constrained natural-language understanding tasks. The system has undergone significant revision since MUC-4, and it is safe to say that the current system does not share a single line of code with the original. The fundamental ideas behind FASTUS, however, are retained in the current system: an architecture consisting of cascaded finite state transducers, each providing an additional level of analysis of the input, together with merging of the final results.

Planning and reacting in uncertain and dynamic environments

Abstract Agents situated in dynamic and uncertain environments require several capabilities for successful operation. Such agents must monitor the world and respond appropriately to important events. The agents should be able to accept goals, synthesize complex plans for achieving those goals, and execute the plans while continuing to be responsive to changes in the world. As events render some current activities obsolete, the agents should be able to modify their plans while continuing activities unaffected by those events. The Cypress system is a domain-independent framework for defining persistent agents with this full range of behaviour. Cypress has been used for several demanding applications, including military operations, real-time tracking, and fault diagnosis.

An intelligent personal assistant for task and time management

We describe an intelligent personal assistant that has been developed to aid a busy knowledge worker in managing time commitments and performing tasks. The design of the system was motivated by the complementary objectives of (1) relieving the user of routine tasks, thus allowing her to focus on tasks that critically require human problem-solving skills, and (2) intervening in situations where cognitive overload leads to oversights or mistakes by the user. The system draws on a diverse set of AI technologies that are linked within a Belief-Desire-Intention (BDI) agent system. Although the system provides a number of automated functions, the overall framework is highly user centric in its support for human needs, responsiveness to human inputs, and adaptivity to user working style and preferences.

The SPARK Agent Framework

There is a need for agent systems that can scale to realworld applications, yet retain the clean semantic underpinning of more formal agent frameworks. We describe the SRI Procedural Agent Realization Kit (SPARK), a new BDI agent framework that combines these two qualities. In contrast to most practical agent frameworks, SPARK has a clear, well-defined formal semantics that is intended to support reasoning techniques such as procedure validation, automated synthesis, and procedure repair. SPARK also provides a variety of capabilities such as introspection and meta-level reasoning to enable more sophisticated methods for agent control, and advisability techniques that support user directability. On the practical side, SPARK has several design constructs that support the development of large-scale agent applications. SPARK is currently being used as the agent infrastructure for a personal assistant system for a manager in an office environment.

A Common Knowledge Representation for Plan Generation and Reactive Execution

Abstract : This paper describes the ACT formalism, which is designed to encode the knowledge required to support both the generation of complex plans and reactive execution of those plans in dynamic environments. ACT is a heuristically adequate representation that is useful in practical applications. It serves as an interlingua for Artificial Intelligence technologies in planning and reactive control. The design of the formalism is discussed and its use in practical applications is demonstrated. These applications show that the ACT representational constructs have reasonable computational properties and also are adequately expressive.

CPEF: A Continuous Planning and Execution Framework

� This article reports on the first phase of the continuous planning and execution framework (CPEF), a system that employs sophisticated plan-generation, -execution, -monitoring, and -repair capabilities to solve complex tasks in unpredictable and dynamic environments. CPEF embraces the philosophy that plans are dynamic, open-ended artifacts that must evolve in response to an ever-changing environment. In particular, plans and activities are updated in response to new information and requirements to ensure that they remain viable and relevant. Users are an integral part of the process, providing input that influences plan generation, repair, and overall system control. CPEF has been applied successfully to generate, execute, and repair complex plans for gaining and maintaining air superiority within a simulated operating environment.

Reasoning with Analogical Representations

Analogical representations have long been of interest to the knowledge representation community. Such representations provide compact encodings of information that can be cumbersome to represent and inefficient to manipulate in sentential languages. In this document, we address the problem of using analogical representations effectively in automated deduction systems. The primary contribution is a formal framework for combining analogical and deductive reasoning. The framework consists of a set of generic operations on analogical structures and accompanying inference methods for integrating analogical and sentential information. The capabilities of the framework are demonstrated for the task of reasoning to extend incomplete maps. The examples presented here have all been solved automatically by an implementation of the integration framework.

Identifying terrorist activity with AI plan recognition technology

We describe the application of plan recognition techniques to support human intelligence analysts in processing national security alert sets by automatically identifying the hostile intent behind them. Identifying the intent enables us to both prioritize and explain the alert sets for succinct user presentation. Our empirical evaluation demonstrates that the approach can handle alert sets of as many as 20 elements and can readily distinguish between false and true alarms. We discuss the important opportunities, for future work, that will increase the cardinality of the alert sets supported by the system to the level demanded by a deployable application. In particular, we outline opportunities to bring the analysts into the process and the opportunities for heuristic improvements to the plan recognition algorithm.

Human directability of agents

Many potential applications for agent technology require humans and agents to work together in order to achieve complex tasks effectively. In contrast, much of the work in the agents community to date has focused on technologies for fully autonomous agent systems. This paper presents a framework for the directability of agents, in which a human supervisor can define policies to influence agent activities at execution time. The framework focuses on the concepts of adjustable autonomy for agents (ie, varying the degree to which agents make decisions without human intervention) and strategy preference (ie, recommending how agents should accomplish assigned task). The directability framework has been implemented within a PRS environment, and applied to a multiagent intelligence-gathering domain.