1. Selmer Bringsjord & Konstantine Arkoudas Andrew Shilliday, Joshua Taylor Sangeet Khemlani, Eric Pratt, Bettina Schimanski, Gabe Mulley Rensselaer AI & Reasoning (RAIR) Laboratory Department of Cognitive Science Department of Computer Science Rensselaer Polytechnic Institute (RPI) Troy NY 12180 USA @ AFRL-R 8.11.05 Remarks on the ongoing... The Poised-for Learning Project

2. Now All on the Poised-for Learning Website

3. Three Driving Objectives 1. Seminal theory of machine learning based on vision of directly inspecting the brain in order to obviate the need for customary post-learning tests. Corresponding implementation that shows theory in action. 2. Seminal theory of machine KR&R that can handle “mental models” and associated notions, which are well-confirmed in cognitive psychology, but not mechanized in AI. Corresponding implementation that can read diagram-rich content and reason over it to arrive at poised-for knowledge. 3. Imbed a & b within context of engineering LbR system.

4. PFL (Overview Figure)

5. The Six Distinguishing Attributes of Poised-For Knowledge Attribute 1: Mixed Representation Mode Symbolic and Diagrammatic Attribute 2: Tapestried Attribute 3: Extreme Expressivity Attribute 4: Mixed Inference Types Attribute 5: Deep Connection to Natural Language Attribute 6: Multi-Agent Structures {} {} “The Eye” demo

6. Militaristic “Wise Man” ** A L E R T ** To: Special Forces Company D From: Central Command, Integrated Special Forces cc: Special Forces Companies A, B, C Recent HUMINT and SIGINT reveals that at least one of you (A, B, C, D), at present, has been locked in as a target of MET's highly effective medium-range laser-guided missile system, the Azan+. Despite the threat this poses (launch could come at any moment), under no circumstances should you change your present location: Any movement could result in your being locked into the sites of the Azan+, if you aren't already. The last thing we want is for a group that isn't locked in to be successfully targeted. As you know, and as the other companies know as well, you cannot determine through use of your EYE system whether your own company has been locked in by the Azan+'s targeting system. But the EYE *can* determine whether *another* company has been locked in (a signature laser tag is visible to the EYE when the Azan+ is aimed at units other than yours). All of you, as you know, can scan each other with the EYE....

7. Militaristic “WM” (con) Company A, upon receiving an alert a few minutes ago informing it that at least one of A, B, C, and D is locked in, and asking it to respond as to whether or not it can infer that it is locked in, engaged its EYE and then sent out comm declaring that it does not know whether it is locked in. After this same comm, B issued the same message, and then C received the same comm and soon thereafter radioed the same message. Now the ball is in your court. As you know, if a company is currently locked in by the Azan+, certain jamming techniques implemented from our location can cloak you once again -- but if these jamming techniques are used mistakenly, if they are used when you are *not* already locked in by the Azan+, you will be immediately targeted, and launch will almost certainly ensue shortly thereafter. We await your response.

8. Wise Man Puzzle

9. Athena Demo

10. Facts Re. Diagrammatic Learning & KR&R The most powerful cognitive systems represent knowledge, and reason over that knowledge, in irreducibly visual/diagrammatic fashion. For confirmation one can consult a good cognitive psychology text, e.g., Goldstein’s Cognitive Psychology. These cognitive systems learn in in large part by reading content that, in turn, is in large part diagrammatic. In some of the texts in our library for the project, more space is devoted to pictographic content than textual content. When it comes to reasoning in support of learning by reading, we now know that there is overwhelming empirical evidence that humans reason is both “proof-theortic” and “mental models-based” fashion (Johnson-Laird, Rips, Bringsjord & Yang).

11. The Dream Blocks World Module Digraphic Module Venn Diagram Module ? Line & Angle Module

12. Engineering Reality Blocks World Module Digraphic Module Venn Diagram Module ? Line & Angle Module

13. Naming Before “Going Public”... DNDL Attributes 1-3 Attributes 4-6... + Athena +...... Vampire...... Paradox...

14. Just a Quick Informal Synopsis; Technical Paper on Site

15. On Poised-for Learning “Core”

16. Poised-For Learning “Core”

17. ? inputoutput prior knowledge, anticipatory queries poised-for knowledge

18. Math Example #5 (”Parallel Lines”) (Gr 7 Textbook) Query Q (TIMSS M8 2003) Q1Q1 Q2Q2 O = (J, A)

19. Math Example #5 (”Parallel Lines”) Query Q (TIMSS M8 2003) O = (J, A)

20. Astronomy Example #1 (”Solar System”) Query Q O = (J, A) Is every planet inside the asteroid belt smaller than the sun?

21. Astronomy Example #1 (”Solar System”) Query Q O = (J, A) Is every planet inside the asteroid belt smaller than the sun?

22. Options Key Distractors

23. ? input output prior knowledge, anticipatory queries poised-for knowledge output attempt to prove option; if successful, save proof; otherwise, disprove, and save disproof

24. Math Example #5 (”Parallel Lines”) Query Q (TIMSS M8 2003) see demos on PFL web site Initial Method

25. Learning Categorization over the Relevant Repository

26. output O = (J, A) Q = (N, S, Opt = (o 1,..., o n )) input rep(N) rep(S) rep(Opt) formalizecategorize C N (rep(N)) C S (rep(S)) C O (rep(Opt)) select key o i key select method M NLG What is learned? rep(J, A)

27. How Complex Can Poised-for Learning Get? Gr 8 PreCalc (this problem is solvable by generating diagrams) The “final frontier” would be scaling up using p-f learning to pose a major problem. See: Bringsjord, S. (1998) “Is Gödelian Model-Based Deductive Reasoning Computational?” Philosophica 61:51-76. Bringsjord, S. (forthcoming) Minds, Machines, Gödel, and Golems ** Various Astronomy Problems **

28. Is PFL really a new, revolutionary form of learning? Apparently. Traditional “knowledge-boosted” learning (RBL, EBL, etc.) is quite primitive by comparison. E.g., EBL gives you only a quantified formula from a particular proof (though it could certainly hand over that proof as well), rather than an arbitrarily complex algorithms with methods as components. And, as Russell & Norvig (2003) explain: Because EBL requires that the background knowledge be sufficient to deduce the hypothesis, “the agent does not actually learn anything factually new from the instance.” (688) Reverse natural-style reasoning is relevant, and will be a component of PFL. As will “creation” of queries (see paper). In the corresponding paper: Why not Turing’s “child-to-adult” AI — but in an academic environment?

29. Stage IV: Implement an Athena/MARMML-based system that automatically generates, from the representation of an answer and accompanying justification rep(A, J) in Stage III, the corresponding output O in English. Stage IV...

31. NDL Proofs to English See wmv & quicktime movies for demos

32. Prior R&D PROVERB But... Taps into “unprincipled” NLG No natural langugage corresponding to diagrammatic knowledge Can’t handle resolution- based reasoning Can’t handle methods, only proofs (not dynamic proofs) Dormant? Reasoning that is input lacks power of Athena

33. We charge on... We are on target for meeting all Year 1 Objectives in PFL Stages I— V.