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									           CALO项目研究进展




2008年10月
              大 纲
 引言
 CALO系统结构
 主要研究内容
    • OAA
    • SPARK
    • IRIS
    • PTIME
    • SR/AR
 展望
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                        引 言 (1)
 项目背景
    • DARPA, 2003, PAL(Perceptive Assistant Learns, 2003~2008)
    • SRI, CALO(Cognitive Assistant that Learns and Organizes)
        Latin word "calonis", which means "soldier’s servant".
 项目目的
    • The goal of the project is to create cognitive software
      systems, that is, systems that can reason, learn from
      experience, be told what to do, explain what they are
      doing, reflect on their experience, and respond robustly
      to surprise.

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                            引 言 (2)
 研究领域
    • Artificial Intelligence, Machine Learning, Natural Language
      Processing, Knowledge Representation, Human-computer
      Interaction, Flexible Planning, and Behavioral Studies

 组织结构
    • 美国斯坦福国际研究院(Stanford Research Institute International,
      简称SRI International)
    • HTTP://www.ai.sri.com/project/CALO, HTTP://caloproject.sri.com/
    • 22家研究机构, 250科研人员



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    引 言 (3)




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    引 言 (4)




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    CALO系统结构 (1)




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    CALO系统结构 (2)




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          CALO系统结构 (3)
 ORGANIZE AND MANAGE INFORMATION
    • 通过收集各种用户信息(电子邮件、月历、文件、项目、联系人等),学习
      出用户所处环境中潜在的关系模型,为更高层次的学习打基础。

 PREPARE INFORMATION PRODUCTS
    • CALO自动将与项目相关的资料如邮件、文档、网页等打包以便用户
      在会议上使用。

 OBSERVE AND MEDIATE INTERACTIONS
    • 包括电子邮件交互、会议交互、多方式的人机交互等,电子邮件交
      互包括对邮件的摘要、分类及排定回复的优先次序等,会议交互包
      括对会议记录进行评注等,多方式的人机交互指综合运用语音、手
      写、笔势、GUI界面操纵等多种方式进行人机交互。

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           CALO系统结构 (4)
 MONITOR AND MANAGE TASKS
     • 对涉及多个子系统和参与者的复杂任务进行协调和管理。

 SCHEDULE AND ORGANIZE IN TIME
     • 帮助用户安排日程、发现时间上的冲突并给出解决建议、代表用户
       和其他人协商会议时间等,并能够学习用户的习惯和具有可调整地
       自主性(用户对日程安排的参与程度)。

 ACQUIRE AND ALLOCATE RESOURCES
     • 发现新的信息来源,学习以及推理角色和专家信息。


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          核心技术
 OAA
 SPARK
 IRIS
 PTIME
 SR/AR    自底向上




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                           OAA (1)
 OAA (Open Agent Architecture)      http://www.ai.sri.com/oaa/




http://www.openagent.com
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                    An Case
      场景:
      • Perrault通过麦克风通知CALO系统: 当关于安全的邮件到达时立
        刻通知我;
      • Cheyer写了一封标题为“security alert”的邮件给Perrault;
      • Perrault在办公室接到了电话,语音提示他有新邮件到达,要他
        输入密码;
      • Perrault通过电话按键输入密码后,系统通过电话播放了邮件的
        内容。

                       DEMO
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     Collaboration Process (1)




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     Collaboration Process (2)




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     Collaboration Process (3)




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     Collaboration Process (4)




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     Collaboration Process (5)




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     Collaboration Process (6)




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                               OAA (2)
 Characteristics [Martin, AAI99] [Cheyer, AAMAS01]
     • Open
         agents can be created in many languages and interface with existing
          systems
     • Extensible
         agents can be added or replaced on the fly
     • User friendly
         high-level, natural expression of delegated tasks
     • Developer friendly
         Unified approach to service provision, data management, and task
          monitoring
     • Multimodal
         handwriting, speech, gestures, and direct manipulation can be
          combined together
     • Reusable
20       Unanticipated sharing across many applications
                               OAA (3)
 ICL (Interagent Communication Language)
     • A layer of conversational protocol defined by event types, similar
       with KQML.
     • A content layer consists of the specific goals, triggers, and data
       elements, similar with KIF.
     • Based on an extension of the Prolog language.
 Event
     • All communications between agents occur in the form of events.
 Trigger
      Provide a general mechanism for specifying some action to be
       taken when some set of conditions is met.

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                              OAA (4)
 Facilitation
     • Delegation, optimization, interpretation
 Declarations of solvables
     • solvable(GoalTemplate, Parameters, Permissions)


     • solvable(send_message(email, +ToPerson, +Params),
       [type(procedure), callback(send_mail)], [])
     • solvable(last_message(email, -MessageId), [type(data),
       single_value(true)], [write(true)])




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                                SPARK (1)
  SPARK (SRI Procedural Agent Realization Kit)
      • PRS, and shares the same Belief Desire Intention (BDI)
        model of rationality.
      • Support the construction of large-scale, practical agent
        systems, and contains sophisticated mechanisms for
        encoding and controlling agent behavior.
      • Has a well-defined semantic model that is intended to
There is a need for agent systems that can scale to real world applications,
        support reasoning about the agents' knowledge and
yet retain the clean semantic underpinning of more formal agent frameworks.
[Morley,execution.
        AAMAS04] [Morley, AAAI04]

http://www.ai.sri.com/~spark/
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             SPARK (2)




     Overall Architecture for a SPARK Agent


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                        SPARK (3)
 Belief
     • A Knowledge base of beliefs about the world and itself that is
       updated both by sensory input from the external world and by
       internal events.
 Procedures
     • provide declarative representations of activities for
       responding to events and for decomposing complex tasks
       into simpler tasks.
 Intentions
     • At any given time the agent has a set of intentions, which are
       procedure instances that it is currently executing.
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                        SPARK (4)
 Executor
     • Is SPARK’s core. Its role is to manage the execution of
       intentions.
     • It does this by repeatedly selecting one of the current
       intentions to process and performing a single step of that
       intention.
     • Steps generally involve activities such as performing tests on
       and changing the KB, adding tasks, decomposing tasks by
       applying procedures, or executing primitive actions.




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                           IRIS (1)
 IRIS: Integrate. Relate. Infer. Share.
     • Semantic Desktop [Cheyer, Semantic Web05]
     • CALO is an artificial intelligence application for which
       IRIS serves as the semantic desktop user interface.

 Integrate
     • Information resources
     • A knowledge base
     • User interface framework
http://www.openiris.org/
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     IRIS (2)




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                             IRIS (3)
 Relate
     • IRIS is used to semantically integrate the tools of
       knowledge work.
     • Clib (the Component Library Specification)
         CALO’s ontology
         Consists of definitions for everyday objects and
          events.
         Use OWL as the data representation.


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                            IRIS (4)
 Infer
     • One of the key differentiators of IRIS, compared to
       many semantic desktop systems, is the emphasis on
       machine learning and the implementation of a plug-and-
       play learning framework.
     • A typical use case
         Email Harvesting.
         Contact/Expertise Discovery.
         Learn from Files.
         Project Creation.
         Classification According to Project.
30       Higher-level Reasoning
                         IRIS (5)
 Share
     • Shared structures are essential for both end-user
       applications, such as team decision making and project
       management,
     • and for infrastructural components such as machine
       learning algorithms, which improve when given larger
       data sets to work on.




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                          PTIME (1)
 PTIME (Personalized Time Management) [Berry,
     AAAI05]
     • PTIME will unobtrusively learn user preferences through a
       combination of passive learning, active learning, and advice-
       taking;
     • As above result, over time the user will become more
       confident of PTIME’s ability, and will thus let it make more
       decisions autonomously;
     • And as autonomy increases, PTIME will learn when to
       involve the user in its decisions.


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                    PTIME (2)
 [Berry, AAMAS06]




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                            PTIME (3)
 Three components of PTIME
     • Process Controller (Heart of PTIME)
         A SPARK agent that captures possible interactions.
         Manages PTIME’s processes, tasking and coordinating the
          activities of the Constraint Reasoner and Preference Learner.
     • Constraint Reasoner
         Explore conflict resolution options using relaxation, event
          bumping, and explanation techniques.
     • Preference Learner
         Is an unobtrusive, online learner where the user’s selections
          from suggested alternatives provide feedback to the learning
          algorithm.
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                        PTIME (4)
 Research Directions [Berry, AAAI05]
     • Soft CSP design [Venable, IJCAI05]
         Simple Temporal Problem (STP)
         Disjunctive Temporal Problem (DTP)
         Simple Temporal Problem with Uncertainty (STPU)
         Disjunctive Temporal Problem with Uncertainty
          (DTPU)
     • Negotiation: Process Design for Conflict Resolution
     • Learning for Adjustable Autonomy
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                       SR/AR (1)
 SR/AR (Situation Assessment / Activity Recognition)
     [Hung 05]
     • Empower CALO with the ability to interpret and make
       sense of what is going on in its environment.
 Tcchnical Challenges
     • Large, dynamic and relational state space.
     • Large sources of temproal and multi-model data.
     • Semantic gaps, uncertainty.


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                           SR/AR (2)
 Research Work
     • T1: Methods for state estimation in relational domains, including
       dealing with unknown number of objects and their identity,
       relevance determination and focus of attention.
     • T2: Methods for inference and learning in continuous time complex
       dynamic processes.
     • T3: Methods for active learning, strategic user querying and fast
       inference in large HMM.
     • T4: Methods for learning and recognizing hierarchical activity
       models from desktop activity traces.

     • T5: Methods for location-based activity recognition.

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                           SR/AR (3)
 Research Work
     • T6: Methods for learning and recognizing activities, gestures and
       relevant objects from low-level physical sensors.
     • T7: Methods for state estimation in communicative activities.
     • T8: Methods for tracking the progress of the CALO plan, including
       possible failures and missed deadlines.




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                           SR/AR (4)
 T1: Situation assessment in relational domain
     • Develop a language for representing domain in which the number
       of objects and their identity is unknown ---- BLOG (Bayesian LOGic)
       and DBLOG (Dynamic BLOG).
     • Propose an approach based on probabilistic relational models that
       does not insist on making a complete propositionalization of the
       domain at inference time.

 T2: Continous time modeling in complex dynamic
  processes
     • From DBN to CTBN (Continuous Time Bayesian Network).


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                               SR/AR (5)
 T3: Active learning, strategic user querying, and fast
  inference in large HMM
     • Have implemented active learning for HMMs and obtained promising
       results on user activity data from an instrumented desktop.
     • Will extend these results to the domain of general graphical models,
       including DBNs.

 T4: Learning and recognizing user’s activities from desktop
  traces
     • Typical user’s activities have an inherent hierarchical structure.
     • The main challenge for CALO is to chain the related events together, and
       infer the hidden sub-activity and activity at the high-level.
     • Efficient inference algorithms and semi-supervised learning
       approach in abstract and hierarchical hidden Markov models, with
       continuous time Bayesian network
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                           SR/AR (6)
 T5: Location-based activity recognition
     • Develop techniques that can reliably estimate the location (Location
       information is extracted from WiFi signal strength).
     • Develop methods for learning and inferring higher-level patterns of
       movement and activities from the data generated by a location-
       aware CALO.
     • From RMNs (Relational Markov Networks) to RFGs (Relational
       Factor Graphs).

 T6, T7 and T8
     • HHMM (Hierarchical Hidden Markov Models) [Nguyen, CVPR05]
     • ProPL (Probabilistic Process Language)

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     SR/AR (7)




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                            展望 (1)
 Transfer Learning         [Dietterich 05]

     • Replacing an employee
         Employee A is leaving an organization and being replaced by
          employee B. Can B’s CALO demonstrate transfer based on
          learning that took place in A’s CALO?

     • Moving to a new job
         An employee leaves organization A and moves to a new
          organization B. Can his CALO demonstrate transfer learning
          from experiences in A to capabilities in B?



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                            展望 (2)
 Some learning mechanisms for transfer learning
     • Hierarchical Bayesian learning
     • Shared parameter models
     • Instance weighting
     • Abstraction regularization
     • Cascading classifiers
     • Attribute Weights and Low Dimensional
       Representations


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                          展望 (3-CALO Learning)
                                           Relational: Learn relationships among entities
Category: Learn relevant groupings                            Sequential: Learn the dynamic structure of
    for observed information                                        ongoing activity of the user

                                                                                    Procedural: Learn to handle
        Language: Learn new                                                         new tasks through planning
      Information from text and     Observation
                                                                  Reflection
             utterances
                                                                                    Factual: Reason to learn
                                                  Inference                                new facts

                                                                           Perceptual: Learn to associate images
                                                  Long-Term                  and sounds with other knowledge
                                                   Memory
                                    Interaction
                                                                   Situational/Episodic
                                                                         Memory
               Advice: Learn from
                    the user


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              展望 (4-Using CALO Learning)
                                                                                                Jean




     Learn when to                                                                Mary          John       Harry




        interact
                                                                                         Learn important
                                                                                          relationships
                                  Timeline
                                                    Inference



                          Interact


                                             MMTM


                         Notice
                                                                       Learn to handle
                                                                         new tasks
                                                                Plan

                                                          Anticipate



Associate people with                                                                        Learn to adapt to
  roles and places                                                                            new situations
                                             Act
                     t                                                                                 t
47                                           Now
               展望 (5-Technical Challenges)
                                                 Robust mixed-initiative multitasking
Enduring improvement                                in a changing environment
  through learning
                                         Timeline

                                                            Introspect
                                      Interact
                                                     MMTM


                                                                         Plan
                                     Notice                                     Anticipate
                                                      Act
  Integration of heterogeneous   t
                                                    Now
      cognitive components

                                                    Establishing and maintaining trust




         Knowing what’s
                                                 Seamless use across platforms
            out there


  48
     Thanks!


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                       参考文献 (1)
 [Morley, AAMAS04] Morley, D. and Myers, K. The SPARK Agent
   Framework. In Proc. of the Third Int. Joint Conf. on Autonomous
   Agents and Multi Agent Systems (AAMAS-04), New York, NY, pp. 712-
   719, July 2004.
 [Morley, AAAI04] Morley, D. and Myers, K. Balancing Formal and
   Practical Concerns in Agent Design. In Proc. of the AAAI Workshop on
   Intelligent Agent Architectures: Combining the Strengths of Software
   Engineering and Cognitive Systems, 2004.
 [Cheyer, Semantic Web05] Cheyer, A. and Park, J. and Giuli, R. IRIS:
   Integrate. Relate. Infer. Share. In 1st Workshop on The Semantic
   Desktop. 4th International Semantic Web Conference, p. 15, Nov 2005.
 [Berry, AAMAS06] Berry, P. and Conley, K. and Gervasio, M. and
   Peintner, B. and Uribe, T. and Yorke-Smith, N. Deploying a
   Personalized Time Management Agent, in Proceedings of the Fifth
   International Joint Conference on Autonomous Agents and Multi Agent
50 Systems (AAMAS’06) Industrial Track, Hakodate, Japan, May 2006.
                         参考文献 (2)
 [Berry, AAAI05] Berry, P. and Gervasio, M. and Uribe, T. and Pollack,
  M. and Moffitt, M. A Personalized Time Management Assistant, in AAAI
  2005 Spring Symposium Series, Stanford, CA, Mar 2005.
 [Venable, IJCAI05] Venable, K. B. and Yorke-Smith, N. Disjunctive
  Temporal Planning with Uncertainty, in Proceedings of Nineteenth
  International Joint Conference on Artificial Intelligence (IJCAI’05),
  Edinburgh, UK, pp. 1385–1386, Aug 2005.
 [Nguyen, CVPR05] Nguyen, N. and Phung, D. and Venkatesh, S. and
  Bui, H. Learning and detecting activities from movement trajectories
  using the hierarchical hidden Markov model, in IEEE International
  Conference on Computer Vision and Pattern Recognition, 2005.
 [Duong, CVPR05] Duong, T. and Bui, H. and Phung, D. and Vekatesh,
  S. Activity recognition and abnormality detecting with the switching
  hidden semi-Markov model, in IEEE International Conference on
  Computer Vision and Pattern Recognition, 2005.
51
                         参考文献 (3)
 [Hung 05] Hung Bui. Situation Assessment and Activity Recognition.
  Technique Report, SRI International, 2005.
 [Dietterich 05] Tom Dietterich, Girish Acharya. Transfer Learning
  Activity for Years 3-5. Technique Report, SRI International, 2005.
 [Martin, AAI99] Martin, David L. and Cheyer, Adam J. and Moran,
  Douglas B. The Open Agent Architecture: A Framework for Building
  Distributed Software Systems. Applied Artificial Intelligence, vol. 13, no.
  1-2, pp. 91-128, January-March 1999.
 [Cheyer, AAMAS01] Cheyer, Adam and Martin, David. The Open
  Agent Architecture. Journal of Autonomous Agents and Multi-Agent
  Systems, vol. 4 , no. 1, pp. 143-148, March 2001.



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