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Learning from Language: It takes an architecture Kenneth D. Forbus Qualitative Reasoning Group Northwestern University.

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Presentation on theme: "Learning from Language: It takes an architecture Kenneth D. Forbus Qualitative Reasoning Group Northwestern University."— Presentation transcript:

1 Learning from Language: It takes an architecture Kenneth D. Forbus Qualitative Reasoning Group Northwestern University

2 Overview Why language needs cognitive architecture –Language for learning & reasoning Companion cognitive systems –Software social organisms Project: Explanation Agent –2 nd generation Learning by reading system Project: Long-lived Learning Software Collaborators –Interactive learning, longevity Wrap-up

3 Goal: Human-level AI Our approach: Build a software social organism –Organisms maintain themselves, with help from their environment and friends –Organisms adapt –Social organisms learn from their culture Accumulating lots of experience is crucial Using language & sketch understanding for accumulating experience & interaction Human experience >> 4×10 6 - 1.5×10 7 ground facts Between 2x and 10x the size of ResearchCyc

4 Language is a blunt instrument Ambiguity is ubiquitous –“Where is the explorer?” Context is crucial Sources of context –Tasks –Experience TREC Q: Amount of folic acid a pregnant woman needs per day? A: Six tons Watson: What is Toronto????

5 Cognitive Architectures Provide Context Examples: SOAR, ACT-R, Companions, Icarus, Polyscheme, Clarion, … Same system, many tasks Provides framing for learning problems –Types of internal decisions provide learning problems Ideally: Learn large bodies of knowledge –Still unsolved, e.g. reading a textbook well enough to answer most of the questions in it would be a landmark Ideally: Work and learn over long periods of time –Still unsolved: Most architectures are used for short experiments and then rebooted

6 The Analogical Mind SME compares examples or generalizations to new situations MAC/FAC retrieves experiences and generalizations from LTM SAGE incrementally produces generalizations Generalizations are probabilistic, partially abstracted, without logical variables 6 Knowledge Base (semantic + experiences + generalizations) SAGE MAC/FAC SME Essence of the Companion cognitive architecture

7 Companion Cognitive Architecture Analogy is central –SME matching –MAC/FAC for retrieval –SAGE for generalization 7 Interaction Manager ResearchCyc contents + other initial endowment Companion cognitive system Executive Session Reasoner Tickler Session Manager Engineering approximations –Agent-based decomposition Can run on cluster or desktop –Logic-based TMS for working memory –HTN planning & execution system LTM, uses MAC/FAC and SAGE Domain Reasoning & Learning Language & Sketch Understanding Self-monitoring & Self-modeling UI, Debugging tools

8 Practical Language Approach (cf. James Allen’s Practical Dialogue approach) Goal: Language as a practical interaction modality Sacrifice syntactic breadth to focus on semantic breadth –Use simplified syntax Simple English Wikipedia, BBC Simple English web site, [Many others] Produce outputs that reasoning systems use –Contrast with usual CL annotate-train-test cycle –Principle: Thousands for representation and reasoning, not one penny for text annotation! Tasks: Cognitive modeling, learning by reading

9 Project: Explanation Agent 2 nd Generation Learning by Reading system Goal: Fully automatic reading of article-length simplified English texts, with sketches –Sources: Hand-translated textbooks, Simple English Wikipedia –At least one book’s worth of material Goal: Self-guided learning –Select & pursue its own learning goals –Seek new articles to read, to fill out background knowledge

10 Research Cyc initial endowment Assimilated knowledge of world history & politics Ruminator Question Generation Strategies Online Reader (DMAP) QA Reasoner Ruminator Simplified texts Parameterized Questions KB Forbus, Riesbeck, Birnbaum, … AAAI 2007 Learning Reader: A 1 st generation LbR system QA Performance: Before reading: 10% After reading: 37% After rumination: 50% Accuracy > 99%

11 EANLU Most cognitive simulations use hand- coded representations Problems –Tailorability –Difficulty scaling –Consistency –Requires expertise Solution: Make production of representations more automatic COMLEX lexicon ResearchCyc KB Contents Allen Parser Query-based Abductive Semantic Interpreter DRT-based Packed Semantics Builder Formal representation of story

12 “ Because of a dam on a river, 20 species of fish will be extinct.” (thereExists (TheList dam19729 river19839) (and (isa river19839 River) (on-Physical dam19729 river19839) (isa dam19729 Dam) (causes-ThingProp dam19729 (thereExists group-of-species19884 (and (isa group-of-species19884 Set-Mathematical) (cardinality group-of-species19884 20) (forAll species19884 (implies (member species19884 group-of- species19884) (thereExists group-of-fish19905 (and (isa group-of-fish19905 Set-Mathematical) (forAll fish19905 (implies (member fish19905 group-of-fish19905) (isa fish19905 Fish))) (generalizes species19884 group-of-fish19905) (isa species19884 BiologicalSpecies))))) (willBe (thereExists be19993 (and (isa be19993 Extinction) (objectActedOn be19993 group-of-species19884))))))))) Universe: dam19729 river19839 (isa river19839 River) (isa dam19729 Dam) (on-Physical dam19729 river19839) (causes-ThingProp dam19729 ) Universe: group-of-species19884 (isa group-of-species19884 Set-Mathematical) (cardinality group-of-species19884 20) (implies-DrsDrs (willBe ) Universe: species19884 (member species19884 group-of-species19984) Universe: group-of-fish19905 (isa species19884 BiologicalSpecies) (generalizes species19884 group-of-fish19905) (isa group-of-fish19905 Set-Mathematical) (implies-DrsDrs ) Universe: be19993 (isa be19993 Extinction) (objectActedOn be19993 group-of-species19884) Universe: fish19905 (member fish119905 group-of-fish19905) (isa fish119905 Fish)

13 CogSketch Learning by Reading w/Diagrams A lever has three basic parts. A fulcrum is a basic part of a lever. A force is a basic part of a lever. A weight is a basic part of a lever. (sketchForDiscourse "kb-resource://" (DrsCaseFn DRS-3446218074-8197)) F is the Fulcrum. E is the force. A2 is the distance between the weight and the fulcrum. A1 is the distance between the force and the fulcrum. A1 is an arm of the lever. A2 is an arm of the lever. Part of Kate Lockwood’s Ph.D. work: After reading simplified NL version of chapter, correctly answers 12/15 homework questions (KCAP09) In vitro, not in vivo A computational version of Mayer’s multimedia learning theory EA NLU SME

14 Automatic Reading Strategies Experience-based filtering –First used in Learning Reader: statistics on joint categorization e.g., no known military actions in DNA adenine base Analogical interpretation –Store disambiguation decisions as cases, reapply by analogy –Use SAGE to generate probabilistic disambiguation rules Conceptual triangulation –Examine how the same ambiguous word is used in multiple similar texts

15 Self-Guided Learning Formulating learning goals –Monitor Q/A performance –Use model-based diagnosis to suggest weaknesses –Set up activities to remedy these weaknesses Learning self-models –How much it knows in various areas –Qualitative models of its own operations Internal inconsistency detection Optimizing use of human attention –Small question budget

16 Towards Long-lived Learning Software Collaborators Interactive Learning. Software needs to be able to learn via apprenticeship: starting as a student, moving to participation in shared work, and increased autonomy and responsibility as it learns. Longevity. Software needs to be able to learn and adapt over substantial periods of time, without intervention by people who know their internals.

17 Strategy games = Rich simulated environment for learning Space –Terrain and its effects –Exploration strategies –Designing/optimizing city placement, transportation networks. Dynamics –Coordinating physical movements of units. –Setting up and maintaining an economy –Balancing short-term versus long-term payoffs, including research –Guns versus butter tradeoffs. Conflict & Cooperation –Military strategy and tactics. –Diplomacy –Joint play Constrained yet broad domain

18 Planned Apprenticeship Trajectory Oh my.. Don’t we need better defenses? Remember when they… Next will serve as aide-de-camp for players, learning from joint play Finally, autonomous operation, playing people on network servers Will get feedback from human mentor Goal: Companions operating as apprentices, for at least a month at a time I got nuked. What should I have done instead? Can you keep Boston from starving? Will start as student, learning basic skills from trainers

19 Learning Spatial Terms Idea: Use SAGE, with multimodal inputs Allow Companion to do active learning, by finding potential examples to label

20 CogSketch Examples 20 Best Generalization IN Size: 3 (candle in bottle, cookie in bowl, marble in water) --DEFINITE FACTS: (rcc8-TPP figure ground) --POSSIBLE FACTS: 33% (Basin ground) 33% (Bowl-Generic ground) Learning spatial prepositions Andrew Lovett’s PhD thesis Kate Lockwood’s PhD thesis Reduces tailorability in spatial stimuli Can copy/paste from PowerPoint

21 Example “This is a choke point” 2 nd order learning goal: How should this example be encoded for SAGE?

22 One Strategy: Intersect with Terrain Model Derive local terrain representations on demand Light Green = Hills Dark Green = Grassland Yellow = Plains Red = Mountains Blue = Water

23 Learning from Stories Tell Companion stories –e.g., the Strait of Hormuz is of strategic importance because it is a choke point for oil distribution. Dispute over the control of it led to a war between Iran and Iraq between 1984 and 1987. Mulling over the story, it should think about –Guarding choke points in its own distribution networks –Find choke points in enemy civs and exploit them –Recognize similar behaviors that arose in games it has already played

24 Extracting Qualitative Models from Language Currently analyzing the Freeciv manual –Objective source of examples of information and advice Goals of the analysis –What sorts of information and advice are communicated via natural language? –What requirements do those impose on NLU systems? –Produce simplified texts that can be used in learning experiments

25 Analysis Results so far Examining three chapters –Cities (89 sentences), terrain (69 sentences), economy (62 sentences) 17% can be parsed into qualitative causal relations –38 original sentences, 76 simplified Other kinds of available knowledge: –Limit points (e.g. “When population reaches zero,…”) –Direct influences (e.g. “while those producing less deplete their granary”) –Extensive/Intensive parameters (e.g. “Any leftover production points…”

26 Experiment: Verifying utility of qualitative knowledge Objective: Build the largest civilization you can in 75 turns. Compare performance under different conditions: –Legal player makes random choices from among legal actions. –Rational player uses a hand-coded qualitative model to make decisions that lead to goal (not necessarily optimal) –Canned strategies follows hand-coded plans to maximize goal.




30 Summary Language evolved as a communication system for intelligent social organisms –To fully participate, our software should also be social organisms –Cognitive architectures provide decision problems that frame learning goals –Potentially provide accumulation of experience Companions cognitive architecture being used to explore –Learning by reading –Interactive learning, longevity

31 What we’re looking for Dialogue system that we can extend –Explanation Agent: Interactive Q/A –Collaborators: Q/A, advice, joint planning –Intelligent tutoring systems Broader coverage parser –We’re reengineering our semantic interpreter this summer Lexicon extensions –Have developed broad-coverage (but uncertain quality) lexicon ourselves

32 Questions? Acknowledgements –ONR Project: David Barbella, Abhishek Sharma –AFOSR project: Tom Hinrichs, Matt McClure, Chris Blair –Other Companions hackers: Scott Friedman –Alums: Matt Klenk, Sven Kuehne, Kate Lockwood, Emmett Tomai

33 How Much Experience? Upper bound = ???, lower bound easier One type of experience: Reading books –Lockwood knowledge capture experiment 8 assertions/simplified English sentence, on average 388 assertions/sketched diagram, on average 8 page chapter with lots of diagrams, ~10K assertions  100 page book, 32K-125K assertions –Suppose a child reads 10 books/year for grades 1-12 4×10 6 if text only, 1.5×10 7 if diagram-heavy Between 2x and 10x the size of ResearchCyc

34 Our Approach to Sketch Understanding Sketching is about spatial relations –Continuous ink understood in qualitative terms –Provides input for spatial and conceptual reasoning Most AI approaches focus only on object recognition –e.g., parts in a circuit schematic –Limited to specific, small domains –Requires training, errors are distracting –Ignores relationships between objects, reasoning about the contents of the sketch Observation: Recognition is not required when people sketch with each other –We verbally label objects as we sketch

35 CogSketch in a nutshell Open-domain sketch understanding system Users draw glyphs, which are given conceptual labels –Recognition is a catalyst, not a requirement –Labels chosen from OpenCyc-derived knowledge base CogSketch performs visual processing to understand visual and spatial relationships in the sketch Analogical mapping, retrieval & generalization built-in Download from:

36 Modeling spatial language learning Analogical generalization can be used to learn several English and Dutch spatial prepositions from sketched input Lockwood et al, Spatial Cognition 2008

37 Evaluation Used publisher provided homework assignment for Basic Machines Test set of 15 multiple choice questions on the topic covered in Chapter 1 (Levers) All questions were hand- translated to predicate calculus Diagrams were sketched using CogSketch System was not allowed to guess if it could not derive an answer Correctly answered 12 out of 15 problems (P < 10 -5 ) Question TypeNumberCorrect TRUE/FALSE22 Simple Query66 Diagram-Concept32 Diagram-Measurement42 TOTAL1512

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