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Question Answering with deep reasoning Chitta Baral, Arizona State U. Michael Gelfond, Texas Tech U. Richard Scherl, Monmouth Univ.
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Text repository Question Domain Knowledge Module N Reasoning Module k Domain Knowledge Module 1 Reasoning Module 1 Processed Text Answer, possibly with explanation
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Core efforts: KR Domain Knowledge Modules –Travel Module -- various Intentions [10:45-11:15] AAAI’05 Generalization : a theory of activities [2:15 – 2:45] –Support structure Fundamental extension of AnsProlog –Consistency restoring (CR-Prolog) -- Sep 04 ASU –Probabilistic reasoning (P-log) -- Tampa Modules –AnsProlog Modules -- Feb 05 ASU –KR Modules (AnsProlog + Prolog + CLP) [11:15-11:45] Interfaces and methodologies –AnsProlog GUI -- Feb 05 ASU –Knowledge Modeling Language -- Feb 05 ASU Further development of action languages – ongoing AAAI’05 (continuous actions, triggers, event ordering, etc.)
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Core efforts: Reasoning Reasoning Modules –Prediction, planning, explanation -- Baltimore –Detecting lies -- TTU thesis –Trying your best -- Feb’05 ASU –Counterfactuals -- ongoing –Reasoning with incomplete information [11:15-11:45] AAAI’05 Explanation generation modules – Explaining an answer set -- ongoing
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Bridge-building efforts – towards an integrated system Logical Form to extracted facts [9:30-10:30] English to Link grammar output to extracted facts [1:45-2:15] Solving Puzzles in English (Bob Leaman’s class project) An end-to-end system with travel module (Matt Hunsaker’s class project) Collaborative Curation -- Feb 05 ASU AQUAINT web site
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Today’s Schedule – 5/11/05 9:00 - 9:20 Overview (Chitta) 9:30 - 10:30 From Logic Forms to ASP query answering (Marcello) 10:45 - 11:15 Reasoning about intentions (Michael) 11:15 - 11:45 New reasoning methods (combining ASP and CSP, approximation algorithm for reasoning with incomplete information) (Michael; Ricardo) 12:00 - 1:30 Lunch 1:45 - 2:15 Link grammar based effort at ASU (Luis) 2:15 – 2:45 From travel modules to a theory of activity -- initial thoughts (Chitta)
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Theory of Activity: A trip A trip has many participants. People can join and leave the trip. There is a start and an end of a trip. A trip may be interrupted resulting in the trip being paused or canceled. A paused trip might be canceled or continued. A trip has a schedule which is often a sequence of actions intended to be performed at particular times. Usually the schedule is followed, and most often if a particular item in the schedule is not possible then it is delayed until it becomes possible. Some time particular items in the schedule may be canceled.
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Example Qs with respect to trips Who are part of a trip at a particular time? What is the status of a trip at a particular time? What are the value of fluents at a particular time? What actions of the trip happened at what time?
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Activities Other examples: –insurrections, terrorism, games, elections, concerts, money-laundering, smuggling, strikes, tournaments, demonstrations, wars, etc. –Activities in the virtual world include work- flows. In most of these activities the actions that are intended to happen are not necessarily scheduled as a sequence.
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QA with respect to an activity narrative Who are the participants of an activity at a particular time? What is the value of a fluent at a particular time? What is the status of an activity at a particular time? Based on what we know what is a viable schedule of an activity? What is the most likely schedule? When did a particular action (part of the activity) happen? Give the interval when a particular action is likely to happen? How to prevent things from going wrong? How to disrupt an activity?
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Knowledge Modeling – objects and properties Trip – basic static attributes – name: a string of characters stops: an ordered list of locations % can be represented as a set of pairs % { (0, locn_0), (1, locn_1)..., (n, locn_n) } % note : locations may be repeated in the list % for trips that include multiple visit % to the same location –derived static attributes – origin: location destination: location intermediate_stops: set of locations number_of_planned_stops: integer
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Objects and properties -- Cont. –basic fluent attributes: position: location union {en_route} %encodes current location last_planned_stop_number: integer participants: set of persons – derived fluent attributes: next_planned_stop: location directly_associated actions: {depart, stop, embark, disembark} directly_associated actions: {embark, disembark} Person – basic static attributes – name: a string of characters position: location Location – basic static attributes – name: a string of characters
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Actions name: a string of characters parameters: set of property classes executability conditions: fluent formulas conditional effects: list of pairs (phi, f) where phi is a fluent formula, and f is a fluent literal actor: triggers: list of pairs (phi, a) where phi is a fluent formula, and a is an action depart is_an action –name: depart –parameter: {trip} –executability conditions: trip.position \neq enroute, trip.number_of_planned_stops < trip.last_planned_stop_number –conditional effects: { (T, trip.position = en_route) }
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Actions (cont.) stop is_an action –name: stop –parameter: {trip.location} –executability condition: trip.position = enroute –conditional effects: { (T, trip.position = trip.location) } embark is_an action – name: embark – parameter: {trip, person} – actor: person – executability condition: trip.participant does_not_include person, trip.position = person.position, trip.position \neq en_route – conditional effects: { (T, trip.participant includes person) } disembark is_an action – name: disembark – parameter: {trip, person} – actor: person – executability condition: trip.participant includes person, trip.position \neq en_route – conditional effects: { (T, trip.participant does_not_include person) }
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Compound actions and constraints go_on is_a compound_action –name: go_on –component actions: {embark, disembark} CONSTRAINTS –% connect various properties. –% for example: The position of a trip and a participant of that trip are always the same.
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