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A Query Mechanism for Inspecting Behavior History Tolga Konik University of Michigan.

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1 A Query Mechanism for Inspecting Behavior History Tolga Konik University of Michigan

2 Goal Storing the behavior history of an agent efficiently Testing conditions on it efficiently

3 Where Can it Be Used? Offline Learning: Hypothesis testing in learning by observation Other types of learning Debugging: Examining the behavior history of a Soar agent Verification: Testing that the agent behavior satisfy user defined constraints

4 Snapshot of Working Memory current-room other sensors AGENT r1 r2 r1 r3 connection “health” type “room1” name w3 item in-room p1 pathdoor 1 distance path destination path destination 2 distance pathdoor p2 path d1d2 d5 in-room door current-room other sensors AGENT

5 Snapshot of Working Memory A Situation: a snapshot of the working memory at a time Agent Behavior History: A Set of Situations

6 Storing Explicit Situations For each situation, store all WMEs Space inefficient

7 Storing Wme Changes Store only the changes of WMEs

8 Storing Indexed Wme Changes Use an index mechanism to efficiently access situations

9 Testing a Condition at a Situation Query: (R1, ^contains-item, ?VALUE) at S 30. Given: R1 is an identifier representing a room S 30 is a situation Result: Return all instantiations of VALUE such that (R1 ^contains-item, VALUE) holds at S 30.

10 For each (Identifier, Attribute) store a binary search tree Testing a Condition at a Situation

11 i1i1 i1i1 i2i2 i1i1 i2i2 i3i3 i1i1 i3i3 i3i3 i2i2 i3i3 25 15 8050 i3i3 65 20 40 start 1520 2540506580 Search tree of (R1, contains-item) Testing a Condition at a Situation

12 i1i1 i1i1 i2i2 i1i1 i2i2 i3i3 i1i1 i3i3 i3i3 i2i2 i3i3 25 15 8050 i3i3 65 20 40 start 1520 2540506580 Query: (R1, ^contains-item, ?VALUE) at VALUE= i1, i2, i3 S30 30 Testing a Condition at a Situation

13 Using this scheme, a rule can be tested at any situation in the history of an agent. This provides space efficiency, while keeping the access times reasonable. We want more than that !

14 Testing a Condition over a Set of Situations GOAL test a rule over a range of situations at once. MOTIVATION In consecutive situations, similar conditions hold RESULT More efficient than testing a rule at each situation individually

15 Testing a Condition on Multiple Situations Query: (R 1, contains-item, ?VALUE) at situations {1-10, 30-45} Output: A Set of (VALUE, Situation-Range) pairs

16 i1i1 i1i1 i2i2 i1i1 i2i2 i3i3 i1i1 i3i3 i3i3 i2i2 i3i3 25 15 8050 i3i3 65 20 40 1-10, 30-45 1-10, 30 – 39 start 1520 2540506580 40 - 45 30 – 39 1-10 40 - 45 Testing a Condition on a Set of Situations

17 i1i1 i1i1 i2i2 i1i1 i2i2 i3i3 i1i1 i3i3 i3i3 i2i2 i3i3 25 15 8050 i3i3 65 20 40 1-10, 30-45 1-10, 30 – 39 start 1520 2540506580 40 - 45 30 – 39 1-10 40 - 45 Testing a Condition on a Set of Situations (R 1 ^contains-item ) at i1i1 i2i2 i3i3 30-39, 40-45 30-39 30-39, 40-45

18 Testing Compound Conditions Compound Conditions C1 and C2 C1 or C2 not C1 Macro Conditions Meta Conditions Temporal Conditions Prolog Programs

19 P(X) X=x 0 Input Situations Output Situations Testing Compound Conditions

20 Q(X=x 0,Y)P(X) X=x 0 Situations D 2 X=x 0, Y=y 0 Situations D 1 Situations D 3 D 1  D 2  D 3 AND Testing Compound Conditions QUERY: P(X) and Q(X,Y) at situations D 1

21 P(X) Situations D 1 Q(Y) Situations D 2 Situations D 3 D 1  D 2 D 1  D 3 X=x 0 Y=y 0 OR Testing Compound Conditions QUERY: P(X) or Q(Y) at situations D1

22 P(X) X is not instantiated Situations D 2 NOT Testing Compound Conditions Situations D 1 D 2 = D 1 – D 3 where D 3 is the set of all situations where P(X) holds QUERY: not P(X) at situations D1

23 Macro Conditions Inspect selected parameters of eaters moves. move(Direction, Content)  top-state(State)and operator(State, Op)and name(Op, move)and direction(Op, Direction) and content(Op, Content)

24 Macro Conditions SituationsDirectionFood Type 2-3eastbonusfood 4-5south... 6-7... 8-9... 10-11westnormalfood 12-13... 14-15...bonusfood QUERY: move(?Direction, ?FoodType)

25 Macro Conditions QUERY: propose-move(?Direction, ?FoodType) SituationsDirectionFood Type 2-3eastbonusfood...northnormalfood...south... 4-5east... southbonusfood...westempty

26 Meta Condition: collect Food TypeSituations normalfood 2-21, 24-27, 30-65, 70-75, 78-83, 86- 109, 114-119, 152-169, … empty 4-5, 10-11, 16-17, 36-37, 42-43, 54-71, 76-77, 96-97, … bonusfood 2-9, 14-35, 40-53, 74-95, 160-165, 204- 207, 214-215, 230-239, … Summarize move operators by food content collect( {X}, propose-move(?,X) )

27 Meta Condition : sort Temporally Reorder sort( {X}, move(?,X) ) SituationsFood Type 2-9bonusfood 10-13normalfood 14-35bonusfood 36-39normalfood 40-53bonusfood 54-65normalfood

28 Meta Conditions : sort Situations Food Type 2-3bonusfood...normalfood 4-5bonusfood...empty...normalfood 6-9bonusfood...normalfood 10-11empty sort( {X}, move-propose(?,X) )

29 Macro Conditions Return the names of top-level operators top-operator(Name)  top-state(State)and operator(State, Op)and name(Op, Name)

30 Macro Conditions any-operator(Name)  state(State)and operator(State, Operator)and name(Operator, Name) state(State)  top-state(State) state(State)  superstate(State, SuperState)

31 Compiling Macro Conditions state(State) Situations Macros can be compiled to index trees for faster access

32 Temporal Conditions So far: return all situations where a set of conditions hold How to match temporal conditions. I.e: “After I have arrived at the office, first, I had a meeting then started working” Location= office meetingworking

33 Temporal Meta Condition : at Situation Intervals are represented explicitly in the representation language. at(C, I 1 ) Conditions that compare intervals after(I 2, I 1 ) ( C is a condition, I 1 and I 2 are situation intervals) I1I2

34 Temporal Meta Condition : at at(location(office), I 1 ) at(have-meeting(Location1), I 2 ) during(I 2, I 1 ) at(working(Location2), I 3 ) during(I 3, I 1 ) after(I 3,I 2 ) The query instantiates regular variables (i.e. Location1, Location2 ) Situation interval variables ( I 1,I 2,I 3 ) Location= office at I 1 meeting at I 2 working at I 3

35 Temporal Conditions Implemented Allen Interval Algebra conditions. (ie. during, starts, meets,...) More complex conditions are possible: I 1 starts within 2 minutes after I 2 ends.

36 Visualization of the Results Two general purpose visualization methods implemented Tables Situation Graphs Domain specific visualization methods are also possible (not implemented)

37 Situation Graph

38 Nuggets A Rich query mechanism for learning, debugging, and verification Compact Storage of Behavior Data Significantly better than explicit storage Proportional to the number of changes (not situations) Fast Access

39 Coals Not connected to Soar nicely: parses debug output All database is in memory Database is not created incrementally Situation Grapher tool implementation is at prototypical stage

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