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Intelligent Agent Technology Jeffrey M. Bradshaw Bob Carpenter Rob Cranfill Mark Greaves Heather Holmback Renia Jeffers Luis Poblete Amy Sun Applied Research.

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Presentation on theme: "Intelligent Agent Technology Jeffrey M. Bradshaw Bob Carpenter Rob Cranfill Mark Greaves Heather Holmback Renia Jeffers Luis Poblete Amy Sun Applied Research."— Presentation transcript:

1 Intelligent Agent Technology Jeffrey M. Bradshaw Bob Carpenter Rob Cranfill Mark Greaves Heather Holmback Renia Jeffers Luis Poblete Amy Sun Applied Research and Technology Shared Services Group The Boeing Company

2 Why Software Agents? Original agent work instigated by researchers studying distributed intelligence New wave of agent research motivated by two practical concerns: – Overcoming the limitations of current user interface approaches – Simplifying the complexities of distributed computing Though each of these problems can be solved in other ways, the aggregate advantage of agent technology is that it can address both of them at once: –by supplementing direct manipulation with indirect management approaches – by building in high-level, loosely-coupled collaborative capabilities out of the box

3 Evolution of System Connectivity

4 Cooperating Systems with Single Agent as Global Planner

5 Cooperating System with Distributed Agents

6 Agent-Enabled System Architecture

7 What is a Software Agent? Agents are software entities that function continuously and autonomously in a particular environment that is often inhabited by other agents and processes Ideally a software agent should be able to: – carry out activities without requiring constant human guidance – learn from its experience – communicate and collaborate with people and other agents – move from place to place over a network as necessary Not all software agents need be intelligent (agents vs. minions) There is no hard dividing line between object technology and multi- agent technology

8 Basic Agent Characteristics Agents adapt to their environment. Dynamic Interaction Alternate Methods Machine Learning Agents cooperate to achieve common goals. Communication Protocols Knowledge-Sharing Coordination Strategies Negotiation Protocols Agents act autonomously to accomplish objectives. Goal-Directed Knowledgeable Persistent Proactive & Reactive Note: Agents can be either static or mobile, depending on bandwidth requirements, data vs. program size, communication latency, and network stability Autonomous Adaptive Cooperative (Dyer, DARPA CoABS)

9 Agents and Objects ObjectsAgents instanceagent unconstrainedknowledge, desires, intentions, capabilities,… operationsmessages defined in classesdefined in suites implicitdefined in conversations nonehonesty, consistency,… Basic unit State-defining parameters Process of computation Message types Message sequences Social conventions (Adapted from Shoham)

10 Applications of Software Agents Office automation/engineering support – mail filtering – meeting scheduling – intelligent assistance – training and performance support Information access – retrieval, filtering, and integration from multiple sources – Internet, intranet, extranet Resource brokering – fair allocation of limited computing resources – dynamic rerouting and reassignment of tasks Active document interfaces – intelligent integration and presentation to suit the task – dynamic configuration according to resource availability and platform constraints Intelligent collaboration – between systems – among people – mixture of people and agent-assisted systems

11 Boeing IAT Program Objectives u More powerful agent frameworks –New KAoS release –UtterKAoS: Conversations, Security, Persistence, Mobility, Middle Agents, Planning –Incorporation of COTS components (e.g., Voyager, Java platform enhancements) u Easier creation of sophisticated agents –ADT, comprised initially of CDT, SDT, PDT u Deploy in spectrum of application areas –Current areas: Information Access, DIG-IT, NASA Aviation Extranet, DARPA JumpStart –New opportunities: Spacecraft autonomy, hybrid networking QoS, security, UCAV, engineering, manufacturing

12 Some Long-Term Requirements for Industrial-Strength Agents u Architecture appropriate for a wide variety of domains and operating environments u Hardware-, operating-system-, programming-language- independent u Separability of message and transport layers u Foundation of distributed-object/middleware (e.g.,CORBA, DCOM) and Internet technologies u Fits well into component integration architectures (e.g., ActiveX, JavaBeans, Web browsers) u Principled extensibility of agent-to-agent protocol u Designed to work with other agent architectures, and to allow easy agentification of existing software u Must be able to incorporate agent interoperability standards as they evolve

13 T KAoS Implementation Context Adaptive Virtual Document Web and other Internet services Link Servers Fine-grained data objects Component tools and services Object Request Broker SGML/XML Component Database Component Multimedia Component Component integration framework Agents CORBA Local and remote databases and services

14 KAoS Structure and Dynamics Formulate/Act on Intentions Life Agent Structure Knowledge Facts Beliefs Desires Intentions Capabilities Update Structure Birth Death Cryogenic State

15 KAoS Extension and Generic Agent

16 Agent-to-Agent Communication Within an Agent Domain

17 Domain Manager and Matchmaker The Domain Manager: Controls entry/exit of agents within a domain, governs proxy agents (i.e., security) Maintains a set of properties on behalf of the domain administrator Provides the address of the Matchmaker to agents within its domain (i.e., naming) The Matchmaker: Helps clients find information about the location of agents that have advertised their services Forwards requests to Matchmakers in other domains as appropriate Can be built on top of native distributed object system services (e.g., trader) Agents Providing Services: Advertise their services to the Matchmaker Are notified by the Matchmaker if their services have been registered Withdraw their services when they no longer wish to provide them Agents Requesting Services Ask the Matchmaker to recommend agents that match certain criteria Are given unique identifiers for the agents that match the criteria Communicate directly with these agents for services

18 Generic Agent GA Mediation Extension Proxy Extension Adapter Matchmaker Extension Domain Mgr. Extension Telesthetic Extension Ext. from Foreign Domain KAoS Agent Domain External Resource Proxy to Another KAoS Domain GA GA = Anatomy of a KAoS Domain

19 Conversations Social interaction is more appropriately modeled when conversations rather than isolated illocutionary acts are taken as the fundamental unit of discourse Two approaches to implementing agent conversations (Walker and Wooldridge): – off-line design: social laws are hard-wired in advance – emergence: conventions develop from within a group of agents KAoS currently provides only for off-line design of conversations, represented as state-transition networks – Shared knowledge about message sequencing conventions enables agents to coordinate frequently recurring interactions of a routine nature simply and predictably. – Cohen and Smiths semantics and joint intention theory have been used to analyze KAoS conversation policies – In the future, more sophisticated agents will either be able to use less constraining landmark-based conversation policies or fall back to more rigid policies with identical semantics to communicate with simpler agents – In support of this, DARPA is funding us to develop a Conversation- Design Tool (CDT)

20 KAoS Conversation Policies u Interaction among agents best modeled at the conversational level, rather than isolated speech acts u Conversation policies are agent dialogue building-blocks that provide a set of constraints that define and restrict what can take place in individual agent conversations –Policies can be expressed via many different representation formalisms, from regular expression grammars to dynamic logics u Conversation policies ensure reliable communication among heterogeneous agents while lessening agents burden of inference –Agents choose between a greatly reduced number of possible conversational moves –Conversation manager (component of generic agent) assures compliance with policy; handles exceptions u References:

21 Conversation for Action Policy Communication about commitments (promise, renege) is handled explicitly, and A can notify B when the request was not fulfilled to its satisfaction (decline report) See formal analysis of Conversation for Action Policy in Smith and Cohen 1996 AAAI paper

22 KAoS Applications DIG-IT: Boeing digital data integration effort to integrate agents in next-generation PMA and BOLD NASA Aviation Extranet: Agent-assisted access to information and services over a large-scale virtual private network AHCPR CDSS Project: Long-term follow-up support for bone marrow transplant patients at the Fred Hutchinson Cancer Research Center DARPA Jumpstart Project: Development of agent design toolkit (Boeing, UWF Cognition Institute, Sun Microsystems, IntelliTek) Agents for space applications: Proposal to use KAoS for a multi-agent testbed in satellite operations, and in the development of a Personal Satellite Assistant (in preparation)

23 JumpStart Project Overview u Selected under the DARPA CoABS Program –Approximately 20 other participants u Partners: Boeing, Sun, UWF, IntelliTek u Collaborator: Oregon Graduate Institute (CHCC) u Deliverables: –Prototype software (CDT and SDT) –Periodic technical reports and demos –Interoperability demos with other CoABS participants

24 DARPAs Vision of the Future of Agents u The Future of Agent Ensembles –Agents authored by different vendors at different times –Wide variety of agent reasoning and action capabilities –Complex operational environment: Unpredictable universe of action Dynamic task-specific agent teams Collaborative, negotiated problem-solving behavior u The Future of Agent Developers –More agents written by domain experts; fewer agents written by agent-technology experts –Decreased ability to control agent contexts of use

25 u Simple agent systems may require only simple models of communication to achieve their ends –Limited tasks, collaborations, interactions with one another –Predictable all simple-agent universe of action –Limited and domain-specific reasoning requirements –Conversations are atomic transactions u Example: –Simple personal information retrieval agents interact mainly with non-agent information sources little negotiation or bargaining Simple Agents May Not Need a Complex Theory

26 u But, consider more complex applications, involving: –Higher reliability, verifiability, precision of expression –Arbitrary, dynamic agent collaboration with negotiation –Unpredictable universe of action –Complex autonomous reasoning about other agents, plans –Extensive human-agent interaction u Examples: –Electronic Commerce/Electronic Trading, Air Traffic Control, Health Care, Military, etc. u This requires a sophisticated multiagent communication model, e.g., conversations, with an explicit semantic foundation. Sophisticated Agents Require Sophisticated Theory

27 Operating in Heterogeneous Environments u Mixture of different agent frameworks u Mixture of simple and sophisticated agents u Approach: shared conversation and security policies, generated off-line, that increase interoperability and robustness in heterogeneous agent environments What Weve Got Here is a Failure To Communicate

28 JumpStart Technical Objectives u Current Focus: Communication and Security tools –More agents written by domain experts; fewer agents written by agent-technology experts –CP scenarios require that agent policy configuration be rapid and robust –Additional classes of development tools needed in future u Help developers design reliable agent conversations –Help develop ACL semantic and pragmatic theory and standards –Provide a prototype conversation design tool (CDT) Aid agent developers in understanding ACL semantics Help select, specialize or generate appropriate conversation policies u Help developers design reliable systems with desired agent security characteristics –Develop foundations for agent security and mobility standards –Provide prototype security design tool (SDT) allowing agent developers to easily select, specialize or generate appropriate agent security policies

29 Conversation Policy Example: Winograd and Flores CFA

30 Combining Finite-State-Based and Plan-Based Conversation Policy Approaches u Intelligent agents can use less constraining plan-based policies that give them flexibility of determining many specifics of conversational moves on-the-fly u Constraints governing plan-based conversation policies make them less complicated to implement than unrestricted agent dialogue models u Simpler agents will continue to rely on more rigidly defined FSM-based policies where the universe of possible moves has been pre-computed off-line u FSM and plan-based versions of same policy must comply to same semantics and pragmatics u Appropriate version can be negotiated between agents at runtime

31 Extending Semantics/Pragmatics u Participate in ongoing ACL development –KAoS, AgentTalk, FIPA, KQML-Lite, KQML-Rite –Ultimate goal of consensus on a compositional semantics with principled extensibility u Analyze the ACL speech acts & conversation policies –We will study/develop basic conversation properties (e.g., the ordering, timing, sequences of communication acts) –Match representations of conversation policies to diverse levels of agent capability: Finite-state-machine models Landmark models Emergent conversations –FSM and landmark models of same policy must comply to same semantics and pragmatics; choice of model negotiated at runtime between agents –We will also investigate other pragmatic conditions imposed by context (e.g., meta-conditions on agent conversations)

32 CDT: An Extensible Java Toolkit for Agent Conversation Design u The CDT is a formal design and verification system for a given theory of agency and ACL u Stanfords OpenProof will be the core framework –OpenProof is a component-based (JavaBeans) formal heterogeneous reasoning environment Allows development of various representations (sentences, reasoning trees, FSMs, Dooley graphs, Petri nets, etc.) Logical fragments (deductive rules, theorem-provers) Heterogeneous transfer rules –Extensible to different logics and theories of agency u Generate resultant conversation policies –Off-line design simplifies agent development and reduces burden of inference for agents at runtime –Policies mediate interaction, helping increase interoperability and robustness in heterogeneous environments

33 Java Security and Mobility u Java is currently the most popular and arguably the most security-conscious mainstream language for agent development u Its cross-platform nature makes it well-suited for heterogeneous environments u However Java failed to address many of the challenges posed by agent software –All or nothing philosophy in sandbox –Lack of fine-grained resource control –Security policy implementation requires writing your own security manager –Applet mechanisms are insufficient for autonomous agent mobility

34 New Developments in Java Security and Mobility u Mechanisms for increasing configurability, extensibility, and fine-grained access control are under development at Sun Microsystems u Java 1.2 enhancements –Applets and applications on equivalent security footings –Finer-grained configurability and better resource control –Specification of much of the security policy via an external policy file, thus separating policy from mechanism u These new developments provide an initial foundation for support of agent-unique requirements

35 Security Design Tool (SDT) u Accelerate incorporation of required agent security and mobility features into the Java platform –Foundation of new Java security model + changes to Java VM –Work with vendors, developers, standards organizations u Issues for Java platform enhancement and SDT development –Agent authentication and PKI management –Secure communication –Enhanced configurability and resource management Denial of service issues: CPU, disk, memory, display Load balancing and grid resource dial –Support for secure agent mobility u SDT Benefits –Configurable starter set of agent security policies –Interoperability among different agent frameworks (grid security dial?) –Faster creation of robust agents by non-experts

36 Agent Scram Capabilities for Anytime Mobility

37 Anytime Mobility u Telescript provided completely transparent agent mobility u Current Java-based agent systems do not –Agent system code runs inside the VM; no access to execution state u Advantages of transparent agent mobility –Agent code need not be structured with many entry points –Allows the agent system (as well as the agents themselves) to move agents between hosts –May be transparent to the agent (may require additional redirection of agent resources) –Supports load balancing of long running agents in the grid u Requires modifications to the Java VM

38 Airplane Troubleshooting Evolution

39 Agent Roles in Technical Information u Agent-Assisted Document Construction At the user-interface, agents work in conjunction with compound document and web browser frameworks and document management tools to select the right data, assemble the needed components, and present the information in the most appropriate way for a specific user and situation. u Agent-Assisted Software Integration Behind the scenes, agents take advantage of distributed object management, database, workflow, messaging, transaction, web, and networking capabilities to discover, link, manage, and securely access the appropriate data and services.

40 Aviation Extranet Goals By the turn of the century, airlines will be able to dynamically reconfigure their flight operations for improved safety and more efficient transportation for the traveling publicBy the turn of the century, airlines will be able to dynamically reconfigure their flight operations for improved safety and more efficient transportation for the traveling public Develop middleware components to integrate and extend the capabilities of aviation legacy systems on a secure extranet to support: – Real-time aircraft and airport situational awareness and scheduling and planning functions – Maintenance and operations procedures enhancements – Feedback data mechanisms to design/manufacturing models and simulators Develop Extranet Global Information Services – Intelligent agents – Metadatabases and Data Warehouses Conduct advanced research in decision support tools for the Aviation Community

41 Aviation Extranet Goals (cont.) Legacy Data Systems CORBA Components Intelligent Agents Data Warehouses Meta-Dbases Decision Support Systems Data Mining Extranet Network Hardware

42 Aviation Extranet Middleware Architecture

43 l Authenticate Once l Permission-Based Access l Encryptable Communication Extranet Security

44 Information Service Agent Metadata/ Ontology Agent Information Broker Agent User Agent Information Service Agent User Agent Information Broker Agent Metadata/ Ontology Agent Information Service Agent Agent-Based Framework for Information Access Information Sources Matchmaker Agent Matchmaker Agent * Matchmaker is connected to almost every agent


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