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CALO Learning Overview. AIC Machine Learning Discussion Group 26 October 2004 with material shamelessly pilfered from previous presentations by: Tom Dietterich/Leslie Kaelbling ( Transfer Learning ) Colin Evans ( Task Setup ) Lynn Voss ( Task Discussion )
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CALO Learning Overview AIC Machine Learning Discussion Group 26 October 2004 with material shamelessly pilfered from previous presentations by: Tom Dietterich/Leslie Kaelbling (Transfer Learning) Colin Evans (Task Setup) Lynn Voss (Task Discussion) David Martin (Task Fulfillment)
The Learning Picture (Terminology) labeled training set annotated corpus execution traces … (test) instance current state document corpus prior knowledge … algorithm input device input meta-learning algorithm Bayesian network HMM decision tree information extractor procedure clusterer … learning algorithm learned device naïve Bayes maximum entropy C4.5 k-means learning by being told … device output predicted categories ranked lists facts/relations social networks clusters …
ML Today: “Engineered” Learning data set algorithm input device input learning algorithm learned device human engineers features, invents algorithms, runs experiments to find the best performance on (static) data sets device output
The Vision: Learning in the Wild ENVIRONMENT algorithm input device input learning algorithm learned device system decides when to learn, what to learn, and how to learn, and adapts itself through interaction with the environment device output
The Vision Behind the Vision: Robust, Enduring Systems CALO immediately performs Task B better transfer learning CALO learns to perform Task A CALO learns to perform Task B faster
Example 1: Transfer of Learned Facts Task A: Meeting Planning Who should attend budget meeting for Project X? Task B: Purchasing Who can approve purchases on Project X? Learned on Task A Learned on Task B Financial officers should attend budget meetings Stephen Q. is financial officer for Project X Financial officers can approve purchases Stephen Q. should attend budget meeting Stephen Q. can approve purchases Transfer Learning, Tom Dietterich & Leslie Kaelbling
Tradeoff Specs, Price, Availability Tradeoff Specs, Price, Availability Computer Meets Specs Availability Shipping Cost Book Meets Specs Availability Shipping Cost Example 2:Transfer of Learned Subprocedures Task A: Purchasing Computers Task B: Purchasing Books • Computer Specs: • CPU speed • Memory size • Disk size • Availability: • Discontinued • Back ordered • Delivery date • Book Specs: • Title • Author • Binding • Availability: • Out of print • Back ordered • Delivery date Transfer Learning, Tom Dietterich & Leslie Kaelbling
Leader Leader Leader Leader Leader Leader Leader Leader Leader Leader Leader Leader Leader Leader Member Member Member Member Member Member Member Member Member Member Member Member Member Member Member Member Member Member Member Member Member Member Member Member Example 3:Transfer of Learned Ontology Task A: Tenure review in university Task B: Command and control in Air Force Organization is a hierarchy of groups Each group has a team leader and team members The members of all groups except the lowest are the team leaders of subgroups Organization is a hierarchy of groups Each group has a team leader and team members The members of all groups except the lowest are the team leaders of subgroups Note: Domain facts and procedures do NOT transfer: Orders flow down hierarchy Tenure dossier flows up hierarchy Transfer Learning, Tom Dietterich & Leslie Kaelbling
Example 4:Transfer of Learned Feature Relevance Task A: Routing Complaints Task B: Meeting Scheduling Job title determines job responsibilities Job title determines job responsibilities Carpenter: framing, installing cabinets Drywaller: taping, sealing, texturing Painter: masking, painting Contractor: scheduling, project planning “Chief Evangelist” might be able to substitute for “Evangelist” in meeting These inferences can be made without even knowing what “sealing” or “Evangelist” mean Transfer Learning, Tom Dietterich & Leslie Kaelbling
CALO Organization • Technology Focus Centers (TFCs) • Reasoning & Action (RA) • Cyber Awareness (CA) • Physical Awareness (PA) • Multi-Modal Dialogue (MMD) • Learning (L) • Scenarios (Year 1) • meeting scheduling • meeting understanding • laptop purchase
Functional Columns (Year 2) • Task Setup • recognize implications of starting a new task • information harvesting, scheduling setup, dossier preparation • Task Discussion • integrate results of interaction between humans and CALOs into task management • meeting understanding • Task Fulfillment • support user in performing tasks • scheduling, procurement
Task Setup: Information Harvesting Task Setup, Colin Evans
Task Setup: Scheduling Setup Task Setup, Colin Evans
Task Setup: Dossier Preparation Task Setup, Colin Evans
User w/ headset Stereo Camera SMART Board Frame CAMEO Frame Frame • Frame includes: • Stereo Camera • (IR - Blue Eyes Camera) • Array Microphones • All attached around a user’s laptop User w/ headset User w/ headset Task Discussion: Meeting Room Task Discussion, Lynn Voss
Body Tracker Speaker localization Face Tracker 3D-Gesture Face Recog. Head, eye, gaze tracker Activity Recog. Affect Recog. Object Recog. Charter Handwriting 2D Gesture Digital Ink Recognizers Video & Array Microphone Classifiers Task Discussion: Architecture Meeting Dossier IRIS Data Store Task Setup Participant List Supporting Docs Topic Agenda Raw Data Capture CAMEO Panoramic MPEG encoder Whiteboard’s Stereo Camera Frame SMART Board Digital Ink Close Talking Speech Instrumented Text Notes & Power Point Meeting Recorder Architecture NTP MSBITS Meeting Playback System Meeting Room Audio Server OOV Agent Suite End Pointer Tracking Data Integrator & Audio Server Prosody OOV Words Transcription DialogueManager Suite Offline Analysis Suite Agenda Topics Multi-parser Tracking Data Integrator Phases Action Items FSDB Roles Rough Sum. Tasks Milestones MS Project Agent MS Project File User Feedback Loop OAA Facilitator Meeting Record / MOKB Meeting Browser Meeting Room IRIS Data Store Purchase Request Task Discussion, Lynn Voss
Task Discussion:Meeting Record Content • Raw Streams • raw audio, raw video, whiteboard strokes, text notes, PPT presentations • Low Level Events • out-of-vocabulary words • participant locations with torso and body positions • participant activities (coarse) • who spoke to whom • recognized affects • recognized words & symbols on the whiteboard • word transcripts • new participants • new chart types • new 2D & 3D gestures
Task Discussion:Meeting Record Content • High Level Events • project plan (task names, durations, milestones) • participants, including entrance/exit • when each agenda item was discussed • topics/subtopics and relevance to agenda • action items, including responsible parties, deadlines • decisions and proposers; alternative proposals and reasons for/against • participant roles (participator, observer, presenter) • meeting phases (introductions, discussions, briefings, presentations)
Task Fulfillment: Scheduling Formulate Scheduling Request (Task Setup) Relax Scheduling Request Get User Selectionsand/or Confirmations Gather Information Update Calendars,Send Notifications Prepare Schedule Candidates Send Reminders Task Fulfillment, David Martin
Task Fulfillment: Purchasing Select Type of Item Get Quotes Relax Query Select Type of Item Get User Selectionsand/or Confirmation LearnVendors AddVendors WrapVendors Refine Purchase Procedure Choose Vendors &Define Requirements Execute Purchase Procedure Task Fulfillment, David Martin
The CALO Test Main Claim: CALO performs well and, through learning, performs even better. • The Test • AP-style exam • Administered regularly throughout the year • Must show general improvement overall. • Only learning in the wild counts.
The CALO Test CALO 2.0 CALO 2.1 CALO 2.2 CALO 2.3 CALO 3.0 total improvement due to engineering and learning Test Score improvement due to engineering improvement due to learning
Situated Learning CALO is a cognitive assistant. • Task Manager (the heartbeat of CALO) • controls what CALO does • situation assessment • workflow management • Knowledge Machine/Query-Update Manager • what CALO knows • CALO ontology
Situated Learning CALO is deployed in the office environment. • IRIS • suite of integrated desktop applications • ontology-driven architecture • provides instrumentation and automation facilities
Learning Issues CALO is not (yet) a robust, enduring system. • much in-the-wild learning is not truly online • concept drift/shift is not addressed • disparate sources are not coordinated • new tasks require human engineering • ontology changes require lobotomies • learning is component-specific