1. Intelligent Software Agents Lab The Robotics Institute Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213-3890 (U.S.A.)

2. OVERVIEW Vision Approach Selected Research Projects

3. Accomplishing Tasks for Humans Augment human teams via RETSINA-guided robots Examples: Robots for urban search and rescue (USAR) Coordination of robots in time-critical missions Reduce the information overload for humans Examples: “Watch for any bad news about stocks in my portfolio.” “Notify me when something will affect my plans.” Human users need only specify high-level objectives Examples: “Find and rescue any human survivors of this collapsed building.” “Plan my trip from Pittsburgh to Trento.”

4. Improve and Diffuse Accessibility Any Time - Any Place Computing –Agents accessible from any device –Appropriateness of Human-Robot Interface (HRI) –Information conveyed on most appropriate device –Information conveyed at most appropriate time Unobtrusive Computing –Reduce the overhead of humans having to specify their intentions –Agents proactively assist humans based on their awareness of the user’s goals and context

5. Transform the Internet to ServiceNet from a network of information providers –user must find information sources –user must integrate information to a network of service providers –agents find requested & unanticipated information for the user –agents perform requested and implied services for the user –agents present finished product to user

6. Achieve Ideals of Software Engineering Truly reusable software components Accessible to lay-programmers –intuitive and imprecise Scalable, reliable, robust, and fault-tolerant computing Program by high-level service requirement descriptions Example: To find the best flights, –find any airline reservation system –that publishes departure / arrival times of four or more commercial airlines and comparative prices for those legs.

7. OVERVIEW Vision Approach Selected Research Projects

8. Approach Consider technologies that will achieve our vision in an economically viable way. Robotic Technologies Network Technologies Sophisticated Natural Language Technologies Human / Agent Interactions Agent / Agent Interactions Agent-Oriented Software Engineering Automatic Learning and Artificial Intelligence

9. Robotic Technologies Team-Oriented Robot Mine Diffusers Robots for Urban Search and Rescue –Physcial USAR lab –Simulated Robotic Search and Rescue Robots that autonomously combine with each other –For climbing stairs or accessing hard to reach areas –Uncouple once the obstacle is surmounted

10. Necessary Network Technologies Local Area Network Discovery –SSDP, SLP Wide Area Network Discovery –Agent-to-Agent Discovery Network Security –protection from malicious attacks and spoofing –Encryption, Authentication, Repudiation Agent Location Schemes –White Pages, Yellow Pages, LDAP

11. Sophisticated Natural Language Technologies Natural Language Understanding and Generation Speech Recognition and Synthesis Information Retrieval, Text Categorization Topic Tracking and Detection, Text Summarization Content and Concept Extraction

12. Human / Agent Interactions Well-considered information presentation and solicitation techniques –Human users may reject non-intuitive agent solutions –Human users do not want to spend their time specifying preferences –Organization and management of context-sensitive preferences –Agents should prefer learning by observing rather than by asking humans Reliable techniques where humans specify and delegate tasks to their agents Understand the nature of human team formation Model human team formation strategies as rules for agents

13. Agent / Agent Interactions Automatic Task Decomposition and Delegation –Consider how agents recognize tasks to delegate Team Coordination and Communication –Evaluate tradeoffs between teaming and not teaming Applicability to Physical Robots –How well does agent situation-awareness improve robot performance? –Which agent coordination strategies are applicable to physical robots?

14. RETSINA Today Assumptions Use Available Resources RETSINA Agent Architecture RETSINA MAS Infrastructure RETSINA MAS Architecture

15. Assumptions Open and Dynamic Environments –agents / services will not always exist –agent locations change system load balancing agent mobility –agent identity changes cannot predict its name cannot predict the vocabulary used to describe it Assume Service Redundancy –multiple/ competing service providers –differentiate on service parameters speed, price, security, reliability, reputation, etc.

16. Use Available Resources For ubiquity, accessibility, scalability, viability Use current and evolving standards –Discovery: SLP, SSDP, DNS, dDNS, Gnutella, etc. –ACLs: KQML, FIPA, DAML, etc. –Representation: XML, HTML, RDF, etc. Agents in any computing environment –Languages: C/C++, Java, Perl, Prolog, Python, etc. –Applications: ModSAF, MSOffice, etc. –Devices: cell phones, PDAs, tablets, laptops, etc. Necessitates a Robust Interface Architecture

17. Four parallel threads : Communicator for conversing with other agents Planner matches “sensory” input and “beliefs” to possible plan actions Scheduler schedules “enabled” plans for execution Execution Monitor executes scheduled plan swaps-out plans for those with higher priorities RETSINA Agent Architecture http://www.cs.cmu.edu/~softagents/retsina.html Reusable Environment for Task-Structured Intelligent Networked Agents

18. MAS Interoperation Translation Services Interoperator Services Capability to Agent Mapping Middle Agents Name to Location Mapping Agent Name Service Security Certificate Authority Cryptographic Service Performance Services MAS Monitoring Reputation Services Multi-Agent Management Services Logging Activity Visualization Launching ACL Infrastructure Public Ontology Protocol Servers Communications Infrastructure Discovery Message Transfer MAS Infrastructure Interoperation Interoperation Modules Capability to Agent Mapping Middle Agent Components Name to Location Mapping ANS Component Security Security Module Private/Public Keys Performance Services Performance Service Modules Management Services Logging and Visualization Components ACL Infrastructure Parser, Private Ontology, Protocol Engine Communication Modules Discovery Message Transfer Modules Individual Agent Infrastructure Operating Environment Machines, OS, Network, Multicast Transport Layer, TCP/IP, Wireless, Infrared, SSL MAS Infrastructure

19. RETSINA Functional Architecture User 1User 2User u Info Source 1 Info Source 1 Interface Agent 1 Interface Agent 2 Interface Agent i Task Agent 1 Task Agent 2 Task Agent t Middle Agent 2 Information Agent n Information Agent n Info Source 2 Info Source 2 Info Source m Info Source m Goal and Task Specifications Results SolutionsTasks Info & Service Requests Information Integration Conflict Resolution Replies Advertisements Information Agent 1 Information Agent 1 Queries Answers

20. Interface Agents Solicit input from user for the agent system Present output to the user Frequently part of task agent Often representative of a device

21. Task Agents Know what to do and how to do it Responsible for task delegation May enlist the help of other task agents

22. Middle Agents Infrastructure agents that aid in MAS scalability Many have been identified in Sycara & Wong ‘00 Most common: –Agent Name Service (White Pages) –Matchmaker(Yellow Pages) –Broker –MAS Interoperator

23. Enable an agent to find another agent: by functionality, capability, availability, time to completion, etc. without knowing who or where the provider agent might be Enables multi-agent systems [MASs]: to dynamically reconfigure themselves to suite a need reduce agent systems administration overhead to scale in the number of agents that are distributed in a computer network RETSINA has two main types of Matchmakers: RETSINA Matchmaker http://www.cs.cmu.edu/~softagents/matchmaker.html Please try it: http://www.cs.cmu.edu/~softagents/a-match/index.html LARKS Matchmaker Language for Advertisement and Request for Knowledge Sharing http://www.cs.cmu.edu/~softagents/larks.html RETSINA Matchmakers

24. The Matchmaking Process MatchmakerRequester Provider 1Provider n 2. Request for service 3. Unsorted full description of (P 1,P 2, …, P k ) 1. Advertisement of capabilities & service parameters 4. Delegation of service 5. Results of service request

25. MAS Interoperators Translate between MAS architectures: Advertisements Queries and replies Informational messages Achieve economic MAS scalability

26. Information Agents Present information sources to MAS Port MAS output to external data stores Represent data and events Four well-known and reusable behaviors: –Single-Shot Query –Active Monitor Query –Passive Monitor Query –Update Query

27. OVERVIEW Vision Approach Selected Research Projects

28. RETSINA supports component reuse across application domains See the ONR JoCCASTA video Also view our CoABS TIE3 video

29. JoCCASTA

30. CoABSControl of Agent-Based Systems NEONon-combatent Evacuation Operation TIE3Technical Integration Experiment 3

31. Agent Storm Simulated in ModSAF Modular Semi- Automated Forces “Real world” events are simulated in Agent Storm by interaction with ModSAF minefield discovery encountering Threat platoon announcements of passed checkpoints RETSINA Mission Agents control ModSAF platoon. route directions marching orders

32. Agent Storm Scenario Threat forces are in retreat Three tank platoon commanders must patrol an area Chase any Threat stragglers out of the area May need to engage if necessary Agents help humans Plan the mission Gather and use intelligence to re-plan mission Actively monitor patrol area during execution De-mine an area

33. http://www.cs.cmu.edu/~softagents/demining.html Without Team-Aware CoordinationWith Team-Aware Coordination Using simple homogenous strategy Robots interfere with each other Robots attempt to de-mine same mine Using simple homogenous strategy and rule that they cannot diffuse the same mine Robots do not interfere with each other A path is more rapidly cleared RETSINA De-mining System

34. MORSE

35. RCAL: RETSINA Calendar Agent and Electronic Secretary

36. MoCHA Mobile Communication of Heterogeneous Agents Anytime, Anywhere Interfaces Context-sensitive preference management Integrates Devices and Agentified Services

37. Warren Portfolio Management Application Tracks price per share and beta values Warns user when portfolio exceeds bounds Provides web search of holdings in portfolio Current research: Text Classification of whether the news article reports good news or bad news about a company.

38. MokSAF Alpha’s Shared Route Charlie’s Shared Route Information about shared routes… Bravo’s Shared Route. Note that this route initially support’s Charlie’s route, then crosses to intercept Alpha’s route.

39. PalmSAF Miniaturized form of MokSAF for hand-held computers Full RETSINA multi-agent system available to PalmSAF user Technical challenges: little memory very few communication ports intermittent communication connections

40. ATLAS / DAML Human and machine-readable web markup Improve web searches via “semantic” indexing Based on: XML, RDF, Oil, Shoe Specify DAML-S Future basis for advertising and ACL

41. RETSINA Visual Recognition Agent http://www.cs.cmu.edu/~softagents/visrec.html Reconnaissance Satellite Agent triggers on asynchronous events recognizes Threat tanks agents autonomously locate it via a Matchmaker agents subscribe to it via the RETSINA Passive Monitor Query RETSINA Information Agent demonstrates that the information agent protocol model is applicable to both data and event sources Used / Reused in Many Projects MURI ‘98 Joccasta CoABS ‘99 NEO TIE MURI ‘00 Agent Storm

42. Prof. Katia Sycara Principle Investigator The Robotics Institute Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213-3890 (U.S.A.) Tel: +1 (412) 268-8825 Fax: +1 (412) 268-5569 katia+@cs.cmu.edu http://www.cs.cmu.edu/~katia Joseph Giampapa Project Manager The Robotics Institute Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213-3890 (U.S.A.) Tel: +1 (412) 268-5245 Fax: +1 (412) 268-5569 garof+@cs.cmu.edu http://www.cs.cmu.edu/~garof Contact Information: