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Biologically Inspired Cognitive Architecture: Why BICA is Necessary for AGI

This talk explores the need for a human-like universal learner, describing human cognition and learning at a higher symbolic level, and the minimal starting requirements for achieving Artificial General Intelligence (AGI). It introduces the GMU-BICA framework, its mental states, and the integration of self-regulated learning in problem solving. The talk also discusses the building of a universal learner and the approaches to building a critical mass.

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Biologically Inspired Cognitive Architecture: Why BICA is Necessary for AGI

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  1. Biologically Inspired Cognitive Architecture Why BICA is Necessary for AGI Alexei Samsonovich (George Mason University)

  2. Questions Answers • Because we need a human-like universal learner • One that describes human cognition and learning at a higher symbolic level • “Critical mass” includes human-like mental states that can act on each other • Why BICA is necessary for achieving AGI? • What kind of a BICA? • What are the minimal starting requirements, i.e., the “critical mass”?

  3. A few words about GMU-BICA

  4. I-Goal: • Stimulus satisfaction • I-Meta: • Scenario • Analysis • I-Past: • Past experience • Prospective memories • I-Imagine: • Intermediate goal situation • I-Previous: • Ideas • Visual input • I-Next: • Scheduled action • Expectation Mental states in GMU-BICA • A mental statein GMU-BICA includes: • Contents of awareness represented by schemas • A token representing an instance of the Self who is aware (labeled I-Now, I-Next, etc.) Working memory: Active mental states of the Self Episodic memory: Frozen mental states of the Self I-Past-1 I-Past-2 • I-Now: • Ideas • Intent I-Past-3 I-Past-4

  5. Mental state dynamics in working memory of GMU-BICA: an example Working memory Input-output me' I-Now S me he he' S He-Now S me me' Semantic memory I-Next He-Next S me me S S R he Q P

  6. Examples of types of mental states in GMU-BICA (a possible snapshot of working memory) I-Goal I-Meta-1 I-Alt-Goal I-Imagined-2 I-Meta-2 I-Imagined-1 I-Imagined-3 I-Subgoal I-Next I-Previous I-Now I-Next-Next I-False-Belief I-Past I-Feel I-Detail-2 I-Past-Revised I-Detail-1 She-Past-Prev She-Past He-Now He-Now-I-Now

  7. Models that we need to integrate

  8. Self-regulated learning (SRL) model of problem solving “…there is a need to build a unified model of meta-cognition and self-regulated learning that incorporates key aspects of existing models, assumptions, processes, mechanisms, and phases” (Azevedo and Witherspoon, AAAI BICA-2008) Action Selection Control Ground Level Object Level Meta- Level Perception Monitoring Doing Reasoning Metareasoning Model of meta-cognition (Cox & Raja, 2007) (based on Zimmerman & Kitsantas, 2006)

  9. Result: A Mental-state model of SRL HW Problem: Solve for x: ax+b=c I-Meta I-Meta-Next Forethought Task analysis Self-beliefs Performance Self-control Self-observation Reflection Self-judgment Self-reactions I-Now I-Next I-Next-Next I-Goal Task analysis Identify goal Select strategic steps (a plan) Self-beliefs Self-efficacy Goal-orientation Intrinsic interest Self-control Enact selected steps to solve the problem Self-observation Self-recording using a worksheet Self-evaluation Compare result to the standard (a template) Self-reaction Met standard Skill mastered Self-reward (Exit) -- OR – Did not meet standard Attribute failure to ineffective strategy selection (Loop reentry) I-Detail-1 I-Detail-2 Homework task Problem: ax+b = c Goal: Solve for x, i.e., have a formula x=… Select strategic steps Isolate x - use subtraction property - use division property Enact strategic steps ax+b = c | -b ax = c-b | /a x = (c-b)/a Result validation x=(c-b)/a compare to x = …(no x in r.h.s.) There is a match. (Samsonovich, De Jong & Kitsantas, to appear in International Journal of Machine Consciousness, 1, June 2009)

  10. Final take-home message

  11. - How to build a universal learner?- Need to bootstrap from “critical mass” ( )- How to build a “critical mass” (suppose we know what)? There are at least three approaches to building a “critical mass”: 1. Incremental bottom-up engineering 2. Brittle rapid prototype-demo 3. SRL assistant (finessing lower levels by students!) Feasible and practically useful stepping stone Without a good stimulus will take forever Useless toy (BICA Phase I) Watch for AAAI 2009 Fall Symposia (BICA, SRL-metacog) Thank you.

  12. End of Talk 1 / Beginning of Talk 2

  13. A Cognitive Map of Natural Language Alexei Samsonovich (George Mason University)

  14. Theory

  15. A B C Introducing two notions of a semantic cognitive map (SCM): • “Strong” SCM with a dissimilarity metric • A is closer to B than C  A is more similar to B than C • “Weak” SCM that captures both synonym and antonym relations B C A A and B are synonyms, A and C are antonyms. Don’t care about unrelated.

  16. Background: Method of building an SCM • Represent symbols (words, documents, etc.) as vectors in Rn • Optimize vector coordinates to minimize H • Do truncated SVD of the resultant distribution dot product x, yQ – vectors in Rn A – antonym pairs S – synonym pairs (Samsonovich & Ascoli, Proceedings of AGI-2007)

  17. Sample N = 10,000 points on a sphere (A) declare some pairs of points ‘synonyms’ (some of those that are close to each other) declare some other pairs of points ‘antonyms’ (some of those that are separated far apart) assign random coordinates to points in 10-dimensional space (B) apply an optimization procedure to the set of 10,000 random vectors in order to minimize the following energy function: The result is the reconstructed spatial distribution of colors (C) Example: color map A B C

  18. Geometric properties of the reconstructed color map are robust with respect to variation of model parameters

  19. Results for Synonym-Antonym Dictionaries

  20. Optimization results

  21. Sorted words and antonym pairs (MS Word English)

  22. Geometric characteristics of the SCM (MS Word English)

  23. Semantic characteristics of the SCM Synonym pairs and antonym pairs, if mixed together, can be separated with 99% accuracy based on the angle between vectors: acute  synonyms, obtuse  antonyms Semantics of the first 3 dimensions are more general than any words, yet clearly identifiable: PC#1: success, positive, clear, makes good sense PC#2: exciting, does not go easy PC#3: beginning, source, origin, release, liberation, exposure synonyms antonyms *

  24. PC-by-PC correlation across languages and datasets

  25. Clustering of words in the first SCM dimension: WordNet and ANEW vs. MS Word

  26. Applications

  27. Examples of “semantic twisting”

  28. Sentiment analysis: 7 utterances automatically allocated on SCM (Samsonovich & Ascoli, in Proc. of AAAI 2008 Workshop on Preference Handling) Please, chill out and be quiet. I am bored and want you to relax. Sit back and listen to me. Excuse me, sorry, but I cannot follow you and am falling asleep. Can we pause? I've got tired and need a break. I hate you, stupid idiot! You irritate me! Get disappeared, or I will hit you! What you are telling me is terrible. I am very upset and curious: what's next? Wow, this is really exciting! You are very smart and brilliant, aren't you? I like very much every word that you say. Please, please, continue. I feel like I am falling in love with you. We have finally found the solution. It looks easy after we found it. I feel completely satisfied and free to go home.

  29. Sentiment analysis:Mapping movie reviews as ‘bags of words’ • Acquired 40+ reviews for each of three movies: Iron Man, Superhero and Prom Night, from the site www.mrqe.com • For each review, computed the average map coordinate of all identified indexed words and phrases. • RESULT: Statistics for PC#1 are consistent with grades given to the movies in the reviews. Iron Man: (1.95, 0.52), Superhero: (1.49, 0.36), Prom Night: (1.17, 0.42) All differences are significant except PC#2 of Superhero vs. Prom Night

  30. CONCLUSIONS Weak SCM is low-dimensional, yet distinguishes almost all synonym-antonym pairs SCM dimensions have clearly identifiable semantics that make sense virtually in all domains of knowledge The map semantics and geometrical characteristics are consistent across corpora and across languages Therefore, SCM can be used as a metric system for semantics (at least for the most general part of semantics) SCM can be used to guide the process of thinking in symbolic cognitive architectures Other potential applications include sentiment analysis, semantic twisting, document search, validation of translation Credits to Giorgio A. Ascoli, Rebecca F. Goldin, Thomas T. Sheehan Thank you.

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