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Adbuctive Markov Logic for Plan Recognition Parag Singla & Raymond J. Mooney Dept. of Computer Science University of Texas, Austin.

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Presentation on theme: "Adbuctive Markov Logic for Plan Recognition Parag Singla & Raymond J. Mooney Dept. of Computer Science University of Texas, Austin."— Presentation transcript:

1 Adbuctive Markov Logic for Plan Recognition Parag Singla & Raymond J. Mooney Dept. of Computer Science University of Texas, Austin

2 Motivation [ Blaylock & Allen 2005] Road Blocked!

3 Heavy Snow; Hazardous Driving Motivation [ Blaylock & Allen 2005]

4 Road Blocked! Heavy Snow; Hazardous DrivingAccident; Crew is Clearing the Wreck Motivation [ Blaylock & Allen 2005]

5 Abduction Given: Background knowledge A set of observations To Find: Best set of explanations given the background knowledge and the observations

6 Previous Approaches Purely logic based approaches [Pople 1973] Perform backward “logical” reasoning Can not handle uncertainty Purely probabilistic approaches [Pearl 1988] Can not handle structured representations Recent Approaches Bayesian Abductive Logic Programs (BALP) [Raghavan & Mooney, 2010]

7 An Important Problem A variety of applications Plan Recognition Intent Recognition Medical Diagnosis Fault Diagnosis More.. Plan Recognition Given planning knowledge and a set of low-level actions, identify the top level plan

8 Outline Motivation Background Markov Logic for Abduction Experiments Conclusion & Future Work

9 Markov Logic [Richardson & Domingos 06] A logical KB is a set of hard constraints on the set of possible worlds Let’s make them soft constraints: When a world violates a formula, It becomes less probable, not impossible Give each formula a weight (Higher weight  Stronger constraint)

10 Definition A Markov Logic Network (MLN) is a set of pairs (F, w) where F is a formula in first-order logic w is a real number

11 Definition A Markov Logic Network (MLN) is a set of pairs (F, w) where F is a formula in first-order logic w is a real number heavy_snow(loc)  drive_hazard(loc)  block_road(loc) accident(loc)  clear_wreck(crew, loc)  block_road(loc)

12 Definition A Markov Logic Network (MLN) is a set of pairs (F, w) where F is a formula in first-order logic w is a real number 1.5 heavy_snow(loc)  drive_hazard(loc)  block_road(loc) 2.0 accident(loc)  clear_wreck(crew, loc)  block_road(loc)

13 Outline Motivation Background Markov Logic for Abduction Experiments Conclusion & Future Work

14 Abduction using Markov logic Express the theory in Markov logic Sound combination of first-order logic rules Use existing machinery for learning and inference Problem Markov logic is deductive in nature Does not support adbuction as is!

15 Abduction using Markov logic Given heavy_snow(loc)  drive_hazard(loc)  block_road(loc) accident(loc)  clear_wreck(crew, loc)  block_road(loc) Observation: block_road(plaza)

16 Abduction using Markov logic Given heavy_snow(loc)  drive_hazard(loc)  block_road(loc) accident(loc)  clear_wreck(crew, loc)  block_road(loc) Observation: block_road(plaza) Rules are true independent of antecedents Need to go from effect to cause Idea of hidden cause Reverse implication over hidden causes

17 Introducing Hidden Cause heavy_snow(loc)  drive_hazard(loc)  block_road(loc) heavy_snow(loc)  drive_hazard(loc)  rb_C1(loc) rb_C1(loc) Hidden Cause

18 Introducing Hidden Cause heavy_snow(loc)  drive_hazard(loc)  block_road(loc) heavy_snow(loc)  drive_hazard(loc)  rb_C1(loc) rb_C1(loc) Hidden Cause rb_C1(loc)  block_road(loc)

19 Introducing Hidden Cause heavy_snow(loc)  drive_hazard(loc)  block_road(loc) heavy_snow(loc)  drive_hazard(loc)  rb_C1(loc) rb_C1(loc)  block_road(loc) accident(loc)  clear_wreck(crew, loc)  block_road(loc) accident(loc)  clear_wreck(crew, loc)  rb_C2(crew, loc) rb_C2(loc, crew) rb_C2(crew, loc)  block_road(loc)

20 Introducing Reverse Implication block_road(loc)  rb_C1(loc) v (  crew rb_C2(crew, loc)) Explanation 2: accident(loc)  clear_wreck(crew, loc)  rb_C2(crew, loc) Explanation 1: heavy_snow(loc)  clear_wreck(loc)  rb_C1(loc) Multiple causes combined via reverse implication

21 Introducing Reverse Implication block_road(loc)  rb_C1(loc) v (  crew rb_C2(crew, loc)) Multiple causes combined via reverse implication Existential quantification Explanation 2: accident(loc)  clear_wreck(crew, loc)  rb_C2(crew, loc) Explanation 1: heavy_snow(loc)  clear_wreck(loc)  rb_C1(loc)

22 Existential quantification Low-Prior on Hidden Causes block_road(loc)  rb_C1(loc) v (  crew rb_C2(crew, loc)) Multiple causes combined via reverse implication -w1 rb_C1(loc) -w2 rb_C2(loc, crew) Explanation 2: accident(loc)  clear_wreck(crew, loc)  rb_C2(crew, loc) Explanation 1: heavy_snow(loc)  clear_wreck(loc)  rb_C1(loc)

23 Avoiding the Blow-up drive_hazard (Plaza) heavy_snow (Plaza) accident (Plaza) clear_wreck (Tcrew, Plaza) rb_C1 (Plaza) rb_C2 (Tcrew, Plaza) block_road (Tcrew, Plaza) Hidden Cause Model Max clique size = 3

24 Avoiding the Blow-up drive_hazard (Plaza) heavy_snow (Plaza) accident (Plaza) clear_wreck (Tcrew, Plaza) drive_hazard (Plaza) heavy_snow (Plaza) accident (Plaza) clear_wreck (Tcrew, Plaza) rb_C1 (Plaza) rb_C2 (Tcrew, Plaza) block_road (Tcrew, Plaza) block_road (Tcrew, Plaza) Pair-wise Constraints [Kate & Mooney 2009] Max clique size = 5 Hidden Cause Model Max clique size = 3

25 Constructing Abductive MLN Given n explanations for Q:

26 Constructing Abductive MLN Given n explanations for Q: 1.Introduce a hidden cause C i for each explanation.

27 Constructing Abductive MLN Given n explanations for Q: 1.Introduce a hidden cause C i for each explanation. 2.Introduce the following sets of rules:

28 Constructing Abductive MLN Given n explanations for Q: 1.Introduce a hidden cause C i for each explanation. 2.Introduce the following sets of rules: Equivalence between clause body and hidden cause. soft clause

29 Constructing Abductive MLN Given n explanations for Q: 1.Introduce a hidden cause C i for each explanation. 2.Introduce the following sets of rules: Equivalence between clause body and hidden cause. soft clause Implicating the effect. hard clause

30 Constructing Abductive MLN Given n explanations for Q: 1.Introduce a hidden cause C i for each explanation. 2.Introduce the following sets of rules: Equivalence between clause body and hidden cause. soft clause Implicating the effect. hard clause Reverse Implication. hard clause

31 Constructing Abductive MLN Given n explanations for Q: 1.Introduce a hidden cause C i for each explanation. 2.Introduce the following sets of rules: Equivalence between clause body and hidden cause. soft clause Implicating the effect. hard clause Reverse Implication. hard clause Low Prior on hidden causes. soft clause

32 Adbuctive Model Construction Grounding out the full network may be costly Many irrelevant nodes/clauses are created Complicates learning/inference Can focus the grounding Knowledge Based Model Construction (KBMC) (Logical) backward chaining to get proof trees Stickel [1988] Use only the nodes appearing in the proof trees

33 Abductive Model Construction Observation: block_road(Plaza)

34 Abductive Model Construction block_road (Plaza) Observation: block_road(Plaza)

35 Abductive Model Construction block_road (Plaza) heavy_snow (Plaza) drive_hazard (Plaza) Observation: block_road(Plaza)

36 Abductive Model Construction block_road (Mall) heavy_snow (Mall) drive_hazard (Mall) Constants: Mall block_road (Plaza) heavy_snow (Plaza) drive_hazard (Plaza) Observation: block_road(Plaza)

37 Abductive Model Construction Constants: Mall, City_Square block_road (City_Square) drive_hazard (City_Square) heavy_snow (City_Square) block_road (Plaza) heavy_snow (Plaza) drive_hazard (Plaza) Observation: block_road(Plaza) block_road (Mall) heavy_snow (Mall) drive_hazard (Mall)

38 Abductive Model Construction Constants: …, Mall, City_Square,... block_road (Plaza) heavy_snow (Plaza) drive_hazard (Plaza) Observation: block_road(Plaza) block_road (Mall) heavy_snow (Mall) drive_hazard (Mall) block_road (City_Square) drive_hazard (City_Square) heavy_snow (City_Square)

39 Abductive Model Construction Constants: …, Mall, City_Square,... Not a part of abductive proof trees! block_road (Plaza) heavy_snow (Plaza) drive_hazard (Plaza) Observation: block_road(Plaza) block_road (Mall) heavy_snow (Mall) drive_hazard (Mall) block_road (City_Square) drive_hazard (City_Square) heavy_snow (City_Square)

40 Outline Motivation Background Markov Logic for Abduction Experiments Conclusion & Future Work

41 Story Understanding Recognizing plans from narrative text [Charniak and Goldman 1991; Ng and Mooney 92] 25 training examples, 25 test examples KB originally constructed for the ACCEL system [Ng and Mooney 92]

42 Monroe and Linux [Blaylock and Allen 2005] Monroe – generated using hierarchical planner High level plan in emergency response domain 10 plans, 1000 examples [10 fold cross validation] KB derived using planning knowledge Linux – users operating in linux environment High level linux command to execute 19 plans, 457 examples [4 fold cross validation] Hand coded KB MC-SAT for inference, Voted Perceptron for learning

43 Models Compared ModelDescription BlaylockBlaylock & Allen’s System [Blaylock & Allen 2005] BALPBayesian Abductive Logic Programs [Raghavan & Mooney 2010] MLN (PC)Pair-wise Constraint Model [Kate & Mooney 2009] MLN (HC)Hidden Cause Model MLN (HCAM)Hidden Cause with Abductive Model Construction

44 Results (Monroe & Linux) MonroeLinux Blaylock94.2036.10 BALP98.80- MLN (HCAM)97.0038.94 Percentage Accuracy for Schema Matching

45 Results (Modified Monroe) 100%75%50%25% MLN (PC)79.1336.8317.4606.91 MLN (HC)88.1846.3321.1115.15 MLN (HCAM)94.8066.0534.1515.88 BALP91.8056.7025.2509.25 Percentage Accuracy for Partial Predictions. Varying Observability

46 Timing Results (Modified Monroe) Modified-Monroe MLN (PC)252.13 MLN (HC) 91.06 MLN (HCAM) 2.27 Average Inference Time in Seconds

47 Outline Motivation Background Markov Logic for Abduction Experiments Conclusion & Future Work

48 Conclusion Plan Recognition – an abductive reasoning problem A comprehensive solution based on Markov logic theory Key contributions Reverse implications through hidden causes Abductive model construction Beats other approaches on plan recognition datasets

49 Future Work Experimenting with other domains/tasks Online learning in presence of partial observability Learning abductive rules from data

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