Detecting and Correcting Errors of Omission After Explanation-based Learning

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1 Detecting and Correcting Errors of Omission After Explanation-based Learning Michael J. Pazzani Department of Information and Computer Science University of California, Irvine, CA IJCAI-89 Monday, August 14, 1989

Pazzani Department of Information and Computer Science

2 Outline I. Problem: A. Detecting errors in generalization produced by EBL B. Assigning blame to rules in domain theory C. Correcting domain theory II. Types of Errors A. Errors of Omission- Fail to make correct prediction B. Errors of Commission - Make incorrect prediction III. Indexing generalizations in Memory A. Explanatory- Given result, Predict cause B. Predictive- Given action, Predict result IV. Unsupervised detection of errors of omission V. Blame Assignment & revising domain theory VI. Experimental Results 2 IJCAI-89 Monday, August 14, 1989

Errors of Commission - Make incorrect prediction III. Indexing generalizations in Memory A. Explanatory- Given result, Predict cause B.

3 Background: OCCAM Performance task Predict outcome of economic sanction incidents Outcome inferred by hierarchical classification Error of omission occurs if incident cannot be classified Error of commission occurs if incident is classified incorrectly coerce s1 s2 s3 s5 s6 s7 s8 Learning Method: Combines empirical and explanation-based learning empirical techniques learn the domain theory used by EBL Problem: incorrect domain theory incorrect generalizations 3 IJCAI-89 Monday, August 14, 1989

classified incorrectly coerce s1 s2 s3 s5 s6 s7 s8 Learning Method: Combines empirical and explanation-based learning

4 Types of examples: Terminology Foundational: examples from which the domain theory is learned E.g., Parent helping child Performance: examples of the performance task E.g., Kidnapping examples Foundational examples are subproblems of the performance task. Performance examples are examples of the performance task. Distinction is relative to task. If task is predicting whom to sell ransom insurance to, the kidnapping examples are foundational. Types of rules (or schemata) Domain: used by EBL to explain performance example Compiled: created by EBL to be used in performance task. 4 IJCAI-89 Monday, August 14, 1989

Distinction is relative to task. If task is predicting whom to sell ransom insurance to, the kidnapping examples are foundational.

5 Problem Statement: Rule in domain theory are learned (or hand-coded) Parents have a goal of preserving their children s health Create compiled rule with EBL One plan to obtain money is to threaten to kill the child of a rich person. Detect error of omission in compiled rule from performance example A kidnapper obtains money from grandparent of hostage. Assign Blame for error on rule in domain theory Revise rule in domain theory Members of the same family have a goal of preserving each other s health Revise Rule in compiled theory 5 IJCAI-89 Monday, August 14, 1989

Detect error of omission in compiled rule from performance example A kidnapper obtains money from grandparent of hostage.

6 Why Errors of Omission? Only type of error created by one-sided learning algorithms Incremental, hill-climbing algorithms (Langley et al., 1987). Accounts for some human learning Grammar: (Berwick, 1986) Concept Acquisition (Bruner, et al, 1956) Subject of theoretical analysis (Valiant, 84; Haussler, 87) Hypothesis never more general than true hypothesis. Incremental blame assignment and revision possible: Error of Omission: One of the domain theory rules used to create a compiled rule needs to be generalized by dropping a condition that is not present in a performance example Error of commission: One of the domain theory rules used to create a compiled rule needs to be specialized by adding a condition that is present in a new performance example. 6 IJCAI-89 Monday, August 14, 1989

7 Unsupervised detection of error of omission coerce s1 s2 s3 s5 s6 s7 s8 Problem: Find a schema in memory that would predict the outcome of a new performance example, if the schema were more general. Approach: Distinguish between two uses of schemata Predictive: What would happen if the United States refused to sell computers to South Korea unless South Korea stopped exporting automobiles to Canada? Explanatory: What could cause the price of oil to rise? 7 IJCAI-89 Monday, August 14, 1989

Approach: Distinguish between two uses of schemata Predictive: What would happen if the United States refused to sell

8 Derivation and use of Indices Derived analytically: explanatory: Features from consequent of domain rules. predictive: Features from antecedent of domain rules. Use during retrieval: explanatory: Finding a schema to explain the cause of an outcome predictive: Finding a schema to predict the outcome of an event. Determining indices: An example Australia and France, 1983 In 1983, Australia refused to sell uranium to France, unless France ceased nuclear testing in the South Pacific. France paid a higher price to buy uranium from South Africa and continued nuclear testing. Processing goal: Explain result (France buys uranium elsewhere) 8 IJCAI-89 Monday, August 14, 1989

Determining indices: An example Australia and France, 1983 In 1983, Australia refused to sell uranium to France, unless France ceased nuclear testing in the South

9 Determining indices: An example 1. Threat -> Increased demand (ACT TYPE (SELL) ACTOR (POLITY EXPORTS?Y ECONOMY (FREE)) TO?X:(POLITY IMPORTS?Y ECONOMY (FREE)) OBJECT?Y:(COMMODITY) MODE (NEG)) 2. Increased demand -> Willingness to pay higher price 3. Purchase -> Possess result (STATE TYPE (DEMAND-INCREASE) ACTOR?X OBJECT?Y) 9 IJCAI-89 Monday, August 14, 1989

X:(POLITY IMPORTS?Y ECONOMY (FREE)) OBJECT?Y:(COMMODITY) MODE (NEG)) 2.

10 Indexing by predictive and explanatory features A B C D coerce actor target... response outcome (coerce actor (polity exports =OBJECT economy (free)) target (polity economic-health (strong) economy (free) imports =OBJECT)... response (act type (sell) actor (polity bus-rel =TARGET exports =OBJECT) object =OBJECT price (money value (>market)) to =TARGET) outcome (goal-outcome type (failure)) 10 IJCAI-89 Monday, August 14, 1989

(strong) economy (free) imports =OBJECT).

11 Detecting an error of omission input: New performance example Hierarchy of schema (domain & compiled theory) Retrieve schema by following predictive indices If schema has outcome, Then If outcome of example and schema agree Then EXIT Else Error of commission Else If example is explainable Then EBL(example) Else If retrieve schema by explanatory indices Then attempt blame assignment Else TDL(example) or SBL(example) Find a schema sufficiently close to the performance example that would explain the example if generalized. 11 IJCAI-89 Monday, August 14, 1989

explainable Then EBL(example) Else If retrieve schema by explanatory indices Then attempt blame assignment Else TDL(example) or SBL(example)

12 Blame assignment (defining sufficiently close ) 1. Reexplain the schema with EBL that maintains dependencies between constraints in the compiled rule and rules in the domain theory. (Trade-off between storing & recomputing): (coerce target (polity economic-health (strong) <- Rule.013 economy (free) <- Rule.012 imports =OBJECT) <- {Rule.01 Rule.13} 2. Find differences between the schema and the new event. 3. Collect inference rules responsible for differences. 4. If one inference rule is responsible for all the differences, then assign blame to this inference rule. 12 IJCAI-89 Monday, August 14, 1989

(Trade-off between storing & recomputing): (coerce target (polity economic-health (strong) <- Rule.013 economy (free) <- Rule.

13 Blame assignment: An example US and USSR, 1980 In 1980, the US refused to sell grain to the Soviet Union if the Soviet Union did not withdraw its troops from Afghanistan. The Soviet Union paid a higher price to buy grain from Argentina and did not withdraw from Afghanistan. 2. Find differences: (coerce target (polity economy (free))) 3. Collect rules: {rule.12} 13 IJCAI-89 Monday, August 14, 1989

The Soviet Union paid a higher price to buy grain from Argentina and did not withdraw from

14 Correcting the domain theory Convert performance example to foundational example: (COERCE ACTOR (POLITY NAME (US) ECONOMY (FREE)...) TARGET (POLITY NAME (USSR) ECONOMY (CONTROLLED)...) THREAT (ACT TYPE (SELL) ACTOR =ACTOR TO =TARGET MODE (NEG)) (ACT TYPE (SELL) ACTOR (POLITY TYPE (COUNTRY) NAME (US) ECONOMY (FREE)...) TO (POLITY TYPE (COUNTRY) NAME (USSR) ECONOMY (CONTROLLED)...) OBJECT (COMMODITY AVAILABILITY (COMMON) TYPE (GRAIN)) MODE (NEG)) RESULT (STATE TYPE (DEMAND-INCREASE) ACTOR (POLITY NAME (USSR)...) OBJECT (COMMODITY TYPE (GRAIN)...) 14 IJCAI-89 Monday, August 14, 1989

..) THREAT (ACT TYPE (SELL) ACTOR =ACTOR TO =TARGET MODE (NEG)) (ACT TYPE (SELL) ACTOR (POLITY TYPE (COUNTRY) NAME (US) ECONOMY (FREE).

15 Correcting the domain theory (cont d) Generalize antecedent of rule to accommodate new example: Old: (ACT TYPE (SELL) ACTOR (POLITY EXPORTS?Y ECONOMY (FREE)) TO?X:(POLITY IMPORTS?Y ECONOMY (FREE)) OBJECT?Y:(COMMODITY) MODE (NEG)) New: (ACT TYPE (SELL) ACTOR (POLITY EXPORTS?Y ECONOMY (FREE)) TO?X:(POLITY IMPORTS?Y) OBJECT?Y:(COMMODITY) MODE (NEG)) Maximally specific conjunctive generalization assumes no noise. 15 IJCAI-89 Monday, August 14, 1989

Y:(COMMODITY) MODE (NEG)) New: (ACT TYPE (SELL) ACTOR (POLITY EXPORTS?Y ECONOMY (FREE)) TO?X:(POLITY IMPORTS?

16 Experimental Design 1.0 All rules that indicate effect of threat were modified by conjoining all preconditions. (cf. Kolodner, 1984) Run on economic sanction database (15, actual, 5 hypo) Accuracy measured after 10 and 15 examples for 10 trials. Accuracy Number of examples occam occam+errors occam+errors+ correction Results OCCAM with error correction more accurate than OCCAM p<.005, t(18)=3.16 OCCAM with correct knowledge base more accurate (few examples) 16 IJCAI-89 Monday, August 14, 1989

17 Related Work Recovering from incorrect knowledge in SOAR (Laird, 1988) patches compiled theory to avoid incorrect knowledge doesn t update domain theory may require patching for each use ML-SMART (Bergadano et al., 1988) Batch system that works on complete set of examples can handle errors of commission supervised Theory Revision (Ginsberg, 1988) Batch system Can handle classification noise supervised can handle errors of commission 17 IJCAI-89 Monday, August 14, 1989

, 1988) Batch system that works on complete set of examples can handle errors of commission supervised Theory

18 Conclusion I. Unsupervised detection of errors of omission requires distinguishing between explanatory and predictive uses of schemata. II. Assigning blame on rules in the domain theory for errors in compiled theory can be accomplished by maintaining dependencies between conditions of compiled rule and a rule in the domain theory 18 IJCAI-89 Monday, August 14, 1989

Detecting and Correcting Errors of Omission After Explanation-based Learning

Detecting and Correcting Errors of Omission After Explanation-based Learning Michael J. Pazzani Department of Information and Computer Science University of California, Irvine, CA 92717 pazzani@ics.uci.edu