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Space: The final frontier Crisis space/resource allocation

Space: The final frontier Crisis space/resource allocation. December 18, 2003. LTI/CSD: Jaime Carbonell, Scott Fahlman, Eugene Fink LTI: Bob Frederking, Greg Jorstad, Ulas Bardak, Thuc Vu, Richard Wang Implicitly: Yiming Yang, William Cohen, Lori Levin, Steve Smith,…. Jaime Carbonell.

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Space: The final frontier Crisis space/resource allocation

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  1. Space: The final frontier Crisis space/resource allocation December 18, 2003 LTI/CSD: Jaime Carbonell, Scott Fahlman, Eugene FinkLTI: Bob Frederking, Greg Jorstad, Ulas Bardak,Thuc Vu, Richard WangImplicitly: Yiming Yang, William Cohen, Lori Levin, Steve Smith,…

  2. Jaime Carbonell Scott Fahlman Space allocation E-mail understanding Eugene Fink Greg Jorstad Ulas Bardak Thuc Vu Bob Frederking Richard Wang People

  3. Satisfy work-related needs ofindividual users and groups • Maximize user satisfaction • Ensure fair allocation of space Purpose Automated allocation of office space and related resources to office users, in both crisis and routine situations.

  4. Alloc.solvable? • Decomposable? • Cope with surprise: not internet-wired, dispersed,… Urgent space allocation Wean Hall Toxic Cloud!

  5. Main steps • Elicitation of user preferences • Near-optimal allocation based onpartial knowledge of preferences • Negotiations with users • Mediation of trades among users

  6. Demo

  7. Main challenges • How to represent and reason about space • How to optimize space allocation conditioned on resources, constraints, preferences, and forecasts • How to cope with surprise, such as crises, degraded space, new constraints, new preferences, new utility functions, and new optimization criteria • How to cope with uncertainty, such as partial knowledge of preferences, contingency planning based on possible exogenous events, and prediction of negotiation outcomes • How to learn what works and why

  8. Knowledge representation • Need to represent: • Facts: People, departments, equipment, affinity groups,… • Spatial relations: 2D and 3D maps, connectivity, functions,… • Constraints: Minimal space/person, labs w/plumbing and power,… • Preferences: Proximity relations, windows, equipment,… • Utilities: Cost and benefit of satisfying preferences, elasticities,… • Episodes: Past space planning with utilities and outcomes, including justifications for decisions, retractions,… • Optimization criteria: As first-class objects, so as to reason about what to optimize and how to assign weights and priorities • Need to reflect on: • What does not the system know that it needs to know: To trigger active learning, user interactions,… • What-if scenarios: For multi-user negotiation, to assess the completeness of the system’s knowledge,…

  9. Pervasive learning • Learning at the factual level • By being told: Constraints, preferences, facts,… • By negotiation and examples: Preference weights, utilities,… • Learning at the planning level • Mode selection: When to plan, when to seek information, when to negotiate, when to optimize, when to validate,… • Operator selection: How to select best actions within modes • Historical learning: What to reuse/transfer longitudinally • Learning at the meta level • Self assessment: Utility of the learning (e.g. idiosyncratic versus general), accuracy of the learning, permanence,… • Targeting the learner: On maximizing expected future discounted utility, on correcting flaws (unlearning),…

  10. First fifteen months • Non-crisis space allocation • Add users to an existing occupied building • Allocate offices in a new building • Respecting constraints and preferences • Unary: Size, windows, internet, bio-isolation,… • N-ary: Proximity to co-workers, quiet,… • Optimizing global utility • Maximize preference satisfaction • Minimize moving users already in place • Coping with uncertainty • Use ranges, defaults, what-if planning • Elicit preferences and trade-offs • Support single-user and multi-user negotiations

  11. Architecture

  12. Main modules • Natural-language e-mail communications • Representation of user preferences, which includes defaults and learned knowledge • Representation of (uncertain) knowledgeof available space and related resources • Optimization based on available knowledge • Intelligent elicitation of user preferencesand information about available space • Single-user and multi-user negotiations • Bartering office space among users • Speed-up and quality learning

  13. Initial results • Understanding of space-related e-mail • Limited representation of space and related resources without uncertainty • Optimization based on simple preferences

  14. Initial results: E-mail understanding Extraction Rules Space E-mail Extraction System Extracted Text Space Template Template Generator

  15. E-mail example Johnson wants to move to wean, he prefers the room 5102. He wants that room for conducting his experiments. His room will be filled with chemical bottles and equipment. He would like to be on the 5th floor, or higher than the 6th floor, but definitely not lower than the fourth floor please. He prefers the size of his room to be between 10–25 square meters. He will be moving into the room starting 2/28/2004 until May 24, 2004. He likes to have at least a window in his room, if possible. His room should have at least 2 doors. His room does not need internet, but definitely need electricity and 5–10 sinks. He would like to be above the Wean Engineering Library, and below his advisor's office. He would also like to be around 50 to 100 yards away from the building's entrance.

  16. Name identifier • Identifies names; tolerant of uncased names • Compared with BBN IdentiFinder as baseline (IdentiFinder was trained on newswire text) • Evaluated on 124 e-mails (test set); precision/recall based on entire name: Recall Precision BBN IdentiFinder 54% 65% Our name identifier 89% 91% Extraction rules • Noun phrase identifier • A rule-based noun phrase chunker utilizing the part-of-speech tags from the tagger

  17. Extraction rules • Negative scope identifier • Determines what part of the sentence has negated meaning • … but not too far away from his classmates… • Quantity identifier • Identifies quantities along with logical attributes • … must be a hundred fifty five square feet…

  18. Extracted text requester: Johnson filler: chemical bottles and equipment purpose: conducting his experiments building: wean room: 5102 date_start: 2/28/2004 date_end: May 24, 2004 floor_min: the 6th floor|the fourth floor floor: the 5th floor size_range: 10–25 square meters window_min: a window entrance_min: 2 doors entrance: the building's entrance plumbing_range: 5–10 internet_neg: internet electric: electricity rel_above: the Wean Engineering Library rel_beneath: his advisor's office dist3_range: 50 to 100 yards dist3_from: the building's entrance

  19. Space template after normalization [Request_Allocation requester: (#Johnson#) filler: (#chemical bottles and equipment#) purpose: (#conducting his experiments#) building: (WEH) room: (5102) floor: (5|>6|>4) size: (>108|<269) start_date: (Sat Feb 28 2004) end_date: (Mon May 24 2004) window: (+) entrance: (+|>2) internet: (-) plumbing: (+|>5|<10) electric: (+) above: (#the Wean Engineering Library#) beneath: (#his advisor's office#) distance: ((>150|<300)(#the building's entrance#)) ]

  20. Initial results: Representation • List of available offices • Database of basic office properties(size, windows, internet connections,…) • On-demand computation of other properties(distance between offices, accessibility,…)

  21. Initial results: Conversion from AutoCad DB RADAR/SpaceRepresentation AutoCad AutoCad maps include line drawings andfree-floating text for office numbers. • Identify the position of each office • Compute the office areas • Identify the office numbers • Find the shortest paths between offices

  22. Initial results: Optimization Application of simulated annealing. • State: Assignment of users to offices; a user may be in a specific office or have no office • Objective function: Weighted count of unsatisfied user preferences • Transitions: • Assign a user to an office • Remove a user from an office • Exchange locations of two users

  23. Future tasks • Representation of relevant knowledge • Uncertain knowledge of user preferences • Uncertain knowledge of available space • Allocation of space and related resources • Possible communications with users • Use of Scone for knowledge representation and inference of implicit information

  24. Future tasks • Representation of relevant knowledge • Understanding of space-related e-mail • Use of Scone knowledge base • Handling multiple requests in one e-mail • Handling user responses to earlier e-mails • Identifying unclear places in e-mails and asking users for clarification

  25. Future tasks • Representation of relevant knowledge • Understanding of space-related e-mail • Generation of e-mail replies • User-friendly explanations • Politeness and diplomacy

  26. Future tasks • Representation of relevant knowledge • Understanding of space-related e-mail • Generation of e-mail replies • Optimization based on partial knowledge • Find an allocation with a (near-)largest expected value of the objective function • Estimate the standard deviation of the resulting expected value • Determine what additional information may reduce the standard deviation

  27. Future tasks • Representation of relevant knowledge • Understanding of space-related e-mail • Generation of e-mail replies • Optimization based on partial knowledge • Preference elicitation and negotiation • Select questions that reduce uncertainty • Estimate probabilities of possible replies • Reduce the number of e-mails to users

  28. Future tasks • Representation of relevant knowledge • Understanding of space-related e-mail • Generation of e-mail replies • Optimization based on partial knowledge • Elicitation of preferences and negotiation • Fairness of space allocation, with respect to • Novice users • Helpful users • Busy users

  29. Future tasks • Representation of relevant knowledge • Understanding of space-related e-mail • Generation of e-mail replies • Optimization based on partial knowledge • Elicitation of preferences and negotiation • Fairness of space allocation • Soft commitments and cancellations • Support different levels of commitment • If breaking a commitment to a user, negotiate appropriate compensation

  30. Future tasks • Representation of relevant knowledge • Understanding of space-related e-mail • Generation of e-mail replies • Optimization based on partial knowledge • Elicitation of preferences and negotiation • Fairness of space allocation • Soft commitments and cancellations • Bartering among users • Allow users to offer office-space trades • Identify prospective multi-user trades

  31. Future tasks • Representation of relevant knowledge • Understanding of space-related e-mail • Generation of e-mail replies • Optimization based on partial knowledge • Elicitation of preferences and negotiation • Fairness of space allocation • Soft commitments and cancellations • Bartering among users • Interaction with human administrators • Providing relevant information • Asking help with complex decisions • Supporting multiple administrators

  32. Future tasks • Representation of relevant knowledge • Understanding of space-related e-mail • Generation of e-mail replies • Optimization based on partial knowledge • Elicitation of preferences and negotiation • Fairness of space allocation • Soft commitments and cancellations • Bartering among users • Interaction with human administrators • Learning new knowledge and strategies • User preferences • Negotiation strategies • E-mail understanding

  33. Future tasks • Representation of relevant knowledge • Understanding of space-related e-mail • Generation of e-mail replies • Optimization based on partial knowledge • Elicitation of preferences and negotiation • Fairness of space allocation • Soft commitments and cancellations • Bartering among users • Interaction with human administrators • Learning new knowledge and strategies • Graphical user interface • Visualization • Spatial input

  34. Schedule of initial versions • Knowledge representation (March 2004) • Understanding e-mail (March 2004) • Generation of e-mail replies (March 2004) • Partial-knowledge optimization (May 2004) • Elicitation of preferences (May 2004) • Fairness of space allocation (December 2004) • Soft commitments and cancellations (2005) • Bartering among users (2005) • Interaction with human administrators (2005) • Learning new knowledge (long term) • Graphical user interface (long term)

  35. Interaction with other systems • Scheduling • Determine the availability of specific users • Anticipate the needs of users and groups • E-mail • Identify and prioritize space-related e-mails • Estimate response times of specific users • Webmaster • Provide on-line information about space • User studies • Evaluate user satisfaction • Improve interaction with users

  36. Uncertainty tolerance • Surprise tolerance • User-friendliness Evaluation • Allocation quality • Speed and scalability

  37. To be continued…

  38. Allocation quality • Maximizing quality of space allocation ai • Subject to time constraints, available extrinsic data, available task information, and communication constraints • Comparison with the results of omniscient unconstrained optimizationaopt

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