Amy Unruh

ACTIVE INFORMATION GATHERING IN INFOSLEUTHTM

InfoSleuth is an agent-based system that can be configured to perform many different information management activities in a distributed environment. InfoSleuthTM agents provide a number of complex query services that require resolving ontology-based queries over dynamically changing, distributed, heterogeneous resources. These include distributed query processing, location-independent single-resource updates, event and information monitoring, statistical or inferential data analysis, and trend discovery in complex event streams. It has been used in numerous applications, including the Environmental Data Exchange Network and the Competitive Intelligence System.

Constructing Robust Conversation Policies in Dynamic Agent Communities

Conversation policies codify allowable exchanges of speech acts among agents as they execute specific types of tasks. Both the set of agents in a community, and the nature of those agents may change over time; however, these agents must conform to a common set of conversation policies that are robust to change and failure. We describe aspects of the implementation of conversation policies in InfoSleuth, including the integral use of finite-state automata for defining those policies. We identify features of those automata and the underlying performatives that are necessary for their robust and correct execution in an operational community. We describe the construction of new conversation policies from simpler underlying components using two mechanisms, extension and concatenation. In this way, we can ensure that the specification of these new policies is easily sharable, and that certain shared characteristics of multiple conversation policies are enforced consistently.

Information aggregation and agent interaction patterns in InfoSleuth

The MCC InfoSleuth/sup TM/ Project is an agent-based system for information gathering and analysis tasks performed over networks of autonomous information sources. A key motivation of the InfoSleuth system is that real information gathering applications require long-running monitoring and integration of information at various levels of abstraction. To this end, InfoSleuth agents enable a loose integration of technologies allowing: (1) extraction of semantic concepts from autonomous information sources; (2) registration and integration of semantically annotated information from diverse sources; and (3) temporal monitoring, information routing, and identification of trends appearing across sources in the information network. In this paper we discuss the agents in InfoSleuth applications and the goal-driven interaction patterns that enable them to dynamically organize and cooperate to perform integrated and temporal information-gathering tasks.

Managing Semantic Compensation in a Multi-agent System

Recovery in agent systems is an important and complex problem. This paper describes an approach to improving the robustness of an agent system by augmenting its failure-handling capabilities. The approach is based on the concept of semantic compensation: “cleaning up” failed or canceled tasks can help agents behave more robustly and predictably at both an individual and system level. However, in complex and dynamic domains it is difficult to define useful specific compensations ahead of time. This paper presents an approach to defining semantic compensations abstractly, then implementing them in a situation-specific manner at time of failure. The paper describes a methodology for decoupling failure-handling from normative agent logic so that the semantic compensation knowledge can be applied in a predictable and consistent way– with respect to both individual agent reaction to failure, and handling failure-related interactions between agents– without requiring the agent application designer to implement the details of the failure-handling model. In particular, in a multi-agent system, robust handling of compensations for delegated tasks requires flexible protocols to support management of compensation-related activities. The ability to decouple the failure-handling conversations allows these protocols to be developed independently of the agent application logic.

Safety and Security in Multiagent Systems: Research Results from 2004-2006

This paper is concerned with assuring the safety of a swarm of agents (simulated robots). Such behavioral assurance is provided with the physics method called kinetic theory. Kinetic theory formulas are used to predict the macroscopic behavior of a simulated swarm of individually controlled agents. Kinetic theory is also the method for controlling the agents. In particular, the agents behave like particles in a moving gas. The coverage task addressed here involves a dynamic search through a bounded region, while avoiding multiple large obstacles, such as buildings. In the case of limited sensors and communication, maintaining spatial coverage – especially after passing the obstacles – is a challenging problem. Our kinetic theory solution simulates a gas-like swarm motion, which provides excellent coverage. Finally, experimental results are presented that determine how well the macroscopic-level theory, mentioned above, predicts simulated swarm behavior on this task.

ARTS: agent-oriented robust transactional system

This paper presents the ARTS (Agent-oriented Robust Transactional System) model, which applies transaction concepts to provide agent developers with high-level support for agent system robustness and reliability. ARTS abstractly considers agents as executors of encapsulated task entities which comply with a set of execution constraints on both normative execution and compensation (repair) semantics. ARTS then defines the task interface in terms of predictable terminating states to support a contract-like interaction among agents. In conjunction with this encapsulation of task semantics, ARTS defines a model for specifying scoped compensation and exception-handling plans for a given task, and for systematically selecting and executing these plans --- triggered by subtask events --- so that the enclosing task semantics are enforced. These capabilities together define a model that reduces design complexity while increasing system robustness, by allowing an agent developer to compose recursively-defined, atomically-handled tasks.