2. AFRL  RAIR Lab Kickoff 6.10.04 Selmer Bringsjord Konstantine Arkoudas, Yingrui Yang, Marc Destefano, Paul Bello, Andy Shilladay, Josh Taylor, Bettina Schimanski Rensselaer AI & Reasoning (RAIR) Laboratory Department of Cognitive Science Department of Computer Science Department of Decision Sciences & Engineering Systems Rensselaer Polytechnic Institute (RPI) Troy NY 12180 USA 6.10.04

3. PART I: RAIR LAB OVERVIEW/TOUR 10a-1230p

4. The Rensselaer AI & Reasoning Lab (The RAIR Lab) A while back, RPI Strategic Investment  Cracking Project; “Superteaching” Slate (Intelligence Analysis) Item generation synthetic characters/psychological time Wargaming hypothesis generation; AI in support of IA

5. Engineering Method for RAIR Lab’s Next-Generation Logic-based AI Isolate and dissect human ingenuity. (psychology of reasoning) Mathematize a weak correlate to this ingenuity courtesy of advanced logical systems. Implement this correlate in working programs. Augment the correlate with machine-specific power.

6. Engineering Method for RAIR Lab’s Next-Generation Logic-based AI Isolate and dissect human ingenuity. (psychology of reasoning) Mathematize a weak correlate to this ingenuity courtesy of advanced logical systems. Implement this correlate in working programs. Augment the correlate with machine- specific power. the thinking enemy our wargamers... But RAIR L overview first...

7. Next-Generation Logic-based AI Reasoning Software: Systems to augment and (sometimes) match human reasoning-based activity. Robot Reasoning: Robots able to accomplish impressive things on the strength of reasoning. The Foundations of AI & CogSci: Are people computers? Does G ö del’s incompleteness results imply that minds are superior to all machines?... PERI

8. Robot Reasoning R&D: PERI (Psychometric Experimental Robotic Intelligence) Scorbot-ER IX Sony B&W XC55 Video Camera Cognex MVS-8100M Frame Grabber Dragon Naturally Speaking Software NL (CARMEL & RealPro?) BH8-260 BarrettHand Dexterous 3-Finger Grasper System

9. Robot Reasoning R&D: PERI

10. Next-Generation Logic-based AI Reasoning Software: Systems to augment and (sometimes) match human reasoning-based activity. Robot Reasoning: Robots able to accomplish impressive things on the strength of reasoning. The Foundations of AI & CogSci: Are people computers? Does G ö del’s incompleteness results imply that minds are superior to all machines?... PERIATP-Powered Bots

11. Next-Generation Logic-based AI Reasoning Software: Systems to augment and (sometimes) match human reasoning-based activity. Robot Reasoning: Robots able to accomplish impressive things on the strength of reasoning. The Foundations of AI & CogSci: Are people computers? Is it possible to formally model the ethical and epistemic attitudes of human beings? What about evil -- can it be mathematized?...

12. Next-Generation Logic-based AI Reasoning Software: Systems to augment and (sometimes) match human reasoning-based activity. Robot Reasoning: Robots able to accomplish impressive things on the strength of reasoning. The Foundations of AI & CogSci: Are people computers? Is it possible to formally model the ethical and epistemic attitudes of human beings? What about evil -- can it be mathematized?...

13. Next-Generation Logic-based AI Reasoning Software: Systems to augment and (sometimes) match human reasoning-based activity. Robot Reasoning: Robots able to accomplish impressive things on the strength of reasoning. The Foundations of AI & CogSci: Are people computers? Does G ö del’s incompleteness results imply that minds are superior to all machines?... e.g., Slate

14. The Slate System (v1.4)

15. Next-Generation Logic-based AI Reasoning Software: Systems to augment and (sometimes) match human reasoning-based activity. Robot Reasoning: Robots able to accomplish impressive things on the strength of reasoning. The Foundations of AI & CogSci: Are people computers? Does G ö del’s incompleteness results imply that minds are superior to all machines?... e.g., Slate Game Development

16. Game Development in the RAIR Lab &

17. Teamed Up w/ VV (and, for ARDA, Planet 9)

18. Next-Generation Logic-based AI Reasoning Software: Systems to augment and (sometimes) match human reasoning-based activity. Robot Reasoning: Robots able to accomplish impressive things on the strength of reasoning. The Foundations of AI & CogSci: Are people computers? Does G ö del’s incompleteness results imply that minds are superior to all machines?... e.g., Slate software for wargaming Game Development

19. The RAIR Lab Offers Six Interconnected Benefits for Wargaming & Military Simulation: The most sophisticated machine reasoners: Athena, MARMML/Chogic, (and “souped up” classics like SNARK, Otter, OSCAR, Vampire) –six attributes tailor-made for the demands of advanced wargaming (handles beliefs, knowledge, ethics, temporal operators, etc.) Symbiotic tie-in, in any R&D conducted for and with AFRL, the ARDA- sponsored Slate system Command over commercial games, including wargames/strategy games, etc. The capacity to build advanced synthetic characters for wargames –on the basis of these machine reasoners, and, for the “easy” processing, ACT-R, Soar The capacity to engineer transparent systems, including transparent virtual environments in which the effects of actions can be completely charted and understood A concrete marriage of the math behind decision-making with the math behind reasoning

20. PART II: OVERVIEW OF LOGIC-BASED AI 1245p-145p

21. Nilsson’s (Simple) Overview

22. Knowledge-Based Agents (AIMA/AIMA2e)

23. J-L 1 Suppose that the following premise is true: If there is a king in the hand, then there is an ace in the hand, or else if there isn’t a king in the hand, then there is an ace. What can you infer from this premise? There is an ace in the hand. NO! In fact, what you can infer is that there isn’t an ace in the hand!

24. Cracked Easily in Natural Deduction

25. Brief Interlude on the Propositional Calculus & First-Order Logic...

26. Scenarios for Intelligence Analysis Wargaming, Simulated C2, Military Simulations

27. “New Order” Microscenario #1 (“no distractor” version) John H. was killed by a member of the Al-Qaeda cell 'The New Order'. The only members of 'The New Order' were John H., Majed H., and Essid D. Within-cell killings only occur when the attacker believes the victim is a traitor, and never when the attacker is of lower rank. Essid D. believes that nobody is a traitor who John H. believes is a traitor. John H. believes everyone except Majed H. is a traitor. Majed H. believes that everyone who is not of lower rank than John H. is a traitor. Majed H. believes everyone is a traitor who John H. believes is a traitor. No one believes everyone in 'The New Order' is a traitor. ‘John H.’ is not an alias for ‘Majed H.’, nor vice versa. In addition, ‘Majed’ isn’t an alias for ‘Essid’ (nor, again, vice versa).

28. Subject Tacking “New Order #1”

29. “New Order” Microscenario #1 (“no distractor” version) John H. was killed by a member of the Al-Qaeda cell 'The New Order'. The only members of 'The New Order' were John H., Majed H., and Essid D. Within-cell killings only occur when the attacker believes the victim is a traitor, and never when the attacker is of lower rank. Essid D. believes that nobody is a traitor who John H. believes is a traitor. John H. believes everyone except Majed H. is a traitor. Majed H. believes that everyone who is not of lower rank than John H. is a traitor. Majed H. believes everyone is a traitor who John H. believes is a traitor. No one believes everyone in 'The New Order' is a traitor. ‘John H.’ is not an alias for ‘Majed H.’, nor vice versa. In addition, ‘Majed’ isn’t an alias for ‘Essid’ (nor, again, vice versa).

30. Solved By Hand in Hyperproof

31. Slate Used to Crack “New Order #1”

32. Using Athena to: Find out who killed; automatically obtain a proof; construct and check a natural deduction-style proof (define culprit-property (forall ?x (iff (culprit ?x) (killed ?x John)))) (assert culprit-property) (find-model (add (exists ?x (culprit ?x)) (ab))) (!prove (killed John John)) ((killed John John) BY (!by-contradiction (assume (not (killed John John)) (dlet ((disjunction (!derive (or (killed Essid John) (killed Majed John)) [(not (killed John John)) premise1 premise2]))) (!by-cases (assume (killed Essid John) (dlet ((S1 (!derive (believesTraitor Essid John) [premise3 premise2 (killed Essid John)])) (S2 (!derive (believesTraitor John John) [premise5 premise9])) (S3 (!derive (not (believesTraitor Essid John)) [S2 premise4]))) (!derive false [S1 S3]))) (assume (killed Majed John) (dlet ((S1 (!derive (believesTraitor Majed John) [premise3 premise2 (killed Majed John)])) (S2 (!derive (believesTraitor John John) [premise5 premise9])) (S3 (!derive (believesTraitor John Essid) [premise5 premise9])) (S4 (!derive (believesTraitor Majed Essid) [S3 premise7])) (S5 (!derive (not (believesTraitor Majed Majed)) [S1 S4 premise8 premise2])) (S6 (!derive (not (lowerRank Majed John)) [(killed Majed John) premise2 premise3])) (S7 (!derive (believesTraitor Majed Majed) [S6 premise6]))) (!derive false [S5 S7]))) [disjunction])))))

33. Denotational Proof Languages (DPLs) DPLs are languages for writing proofs and proof tactics in arbitrary logics Novel syntax and semantics (based on the abstraction on assumption bases) ensure: –Readability and writability –Efficient proof checking –Guaranteed soundness –Powerful mechanisms for expressing complex proof tactics and tacticals

34. Wide applicability DPLs have been designed and implemented for: –Classical logics (both first- and higher-order) –Intuitionist logics –Modal and temporal logics –Program logics (Hoare-Floyd logics) –Type systems

35. Athena A DPL for classical first-order logic Uses natural deduction Incorporates a higher-order functional programming language with algebraic data types Supports induction, recursion, pattern matching Other logics (e.g. modal logic) can be rapidly prototyped by implementing them on top of Athena

36. PART III: WARGAMING AND ADVANCED SYNTHETIC CHARACTERS 145p-230p

37. PART IV: WARGAMING AND ADVANCED SYNTHETIC CHARACTERS 230p-330p

38. Wargaming Formalized? Don’t yet have a formal account. –lots of books and papers... but not a lot of rigor But -- we know that agents are required. We want agents that have human-level thinking power: –We want advanced synthetic characters for wargaming and military simulations –We want to model the mindset of terrorists, replete with their ethical norms, vs. ours, and replete with what they believe about us, what they believe about what we believe, what we believe about what they believe about what we believe, and so on

39. Building a Taxonomy of Wargames

42. Advanced Synthetic Characters: Background Reading A classic originally published in 1946. Egri shows that at the core of all good dramatic writing (whatever its form) stand not rules for cranking out text, but fully developed characters. This book introduces the so-called "dialectical method," and connects it to case studies created on the fly, and to great drama of the past (e.g., Ibsen, and e.g. his immortal Nora). From the standpoint of AI and the creation of advanced synthetic characters, the book is daunting, as it asserts that to be a decent playwright one must have a monstrous amount of knowledge drawn from psychology, sociology, economics, and so on. Kress breaks down the complex art of writing into numerous techniques of representation. The first third of the book concentrates on techniques of characterization. From an AI stance, the book presents a few interesting challenges: it presents evidence that a character’s exterior presentation must be tightly bound to his or her history, and it asserts that all truly developed characters must be based on the author’s own internal emotional state and life experiences. An adaptation of Stanislavsky’s Method for actors to writing, Collins focuses on the presentation of characters within narrative. The techniques it includes for demonstrating emotion through action appear readily applicable to the representational aspects of ASCs. However, much of the information about specific emotion is assumed to be drawn from the author’s personal life experiences, making some of its techniques difficult to apply. Halperin’s eight chapters can be considered as separate essays, each tackling one aspect of characterization. Of particular use in ASCs are the chapters on interior motivation and cultural legacy, which provide useful “template” information to set a character within an internal and social context.

43. A dictionary of character details, McCutcheon is of primary use in populating knowledge bases for use in character generation. It provides a useful reference of physical traits, mannerisms, modes of dress and common names from which the groundwork for deeper representation could be laid. Edelstein is a “cookbook” of character traits, organized by types of characters they are appropriate to. Within the confines of AI research, it useful in the sub-categorization of broad character traits into more specific associated details. Hood provides a sequence of 3-4 page treatments of specific emotions, focusing on how to convey them effectively in prose. Although the book is primarily concerned with the language used to represent them, its discussion of emotional impact on behavior makes it useful in the generation of ASCs. Primarily a guide to script-writing, Wolff contains a single chapter on creating three-dimensional characters that provides a first-draft structure for representing knowledge about a character. The current under-construction vMEM ASC is based on the 31-question overview of a character provided here as a starting point on which to base a Q/A system. A collection of short essays on writing, Dickson contains a great deal of shallow and stereotypical information. Many of its essays on characterization are better considered as tactics to avoid using – they seem to favor quick solutions over deep representation. However, its discussion of the importance of central traits and character flaws in creating empathy is significant for the deep representation of such traits in ASCs. Adv. Synthetic Characters: Background Read. Con.

44. Synthetic Characters To Leapfrog?

45. Same Thing Here: Definitely Not an Advanced SC! Every behavior that happens in The Sims is computed from a number (1- 10) for each attribute.

46. Where’s the cognition?

47. RASCALS ecumenical

48. RASCALS logic-based First-Order Logic Subsumption-Based Architecture

49. First Steps (w/ contract, SOW, $) Model “deontically intense” situation in game; implement; demonstrate (for our sponsors); refine; model... Model “epistemically intense” situations in logicist fashion; implement; demonstrate (for our sponsors); refine; model...

50. THE END

51. Decision-making meets Reasoning... “ MARMML and Newcomb’s Problem” (separate ppt and papers)

52. Slate Hypothesis Generation in our Narrative Scenario (  v) What is the destination of the convoy? customary destinations ruled out ---------------- PROOF ---------------- 1 [] -Yar(x)|Terrorists(x). 2 [] -WindAccessible(x,y)| -USBase(x)| -Bioagents(z)| -Terrorists(z)|AttackPosition(y,z,x). 3 [] -CaveSystem(x,aconvoy)| -Accessible(x,aconvoylocation). 4 [] -Camp(x,aconvoy)| -Accessible(x,aconvoylocation). 5 [] -Village(x,aconvoy)| -Accessible(x,aconvoylocation). 6 [] -AttackPosition(x,y,z)| -Convoy(y)| -Terrorists(y)| -PresentLocation(y,u)| -Accessible(x,u)|CaveSystem($f1(x,y,u,z),y)|Village(z2,y)|Camp(z3,y)|Destination(x,y). 7 [] -AttackPosition(x,y,z)| -Convoy(y)| -Terrorists(y)| -PresentLocation(y,u)| -Accessible(x,u)|CaveSystem($f1(x,y,u,z),y)|Village(z2,y)|Accessible(z3,u)|Destination(x,y). 8 [] -AttackPosition(x,y,z)| -Convoy(y)| -Terrorists(y)| -PresentLocation(y,u)| -Accessible(x,u)|CaveSystem($f1(x,y,u,z),y)|Accessible(z2,u)|Camp(z3,y)|Destination(x,y). 9 [] -AttackPosition(x,y,z)| -Convoy(y)| -Terrorists(y)| -PresentLocation(y,u)| -Accessible(x,u)|CaveSystem($f1(x,y,u,z),y)|Accessible(z2,u)|Accessible(z3,u)|Destination(x,y). 10 [] -AttackPosition(x,y,z)| -Convoy(y)| -Terrorists(y)| -PresentLocation(y,u)| -Accessible(x,u)|Accessible($f1(x,y,u,z),u)|Village(z2,y)|Camp(z3,y)|Destination(x,y). 11 [] -AttackPosition(x,y,z)| -Convoy(y)| -Terrorists(y)| -PresentLocation(y,u)| -Accessible(x,u)|Accessible($f1(x,y,u,z),u)|Village(z2,y)|Accessible(z3,u)|Destination(x,y). 12 [] -AttackPosition(x,y,z)| -Convoy(y)| -Terrorists(y)| -PresentLocation(y,u)| -Accessible(x,u)|Accessible($f1(x,y,u,z),u)|Accessible(z2,u)|Camp(z3,y)|Destination(x,y). 13 [] -AttackPosition(x,y,z)| -Convoy(y)| -Terrorists(y)| -PresentLocation(y,u)| -Accessible(x,u)|Accessible($f1(x,y,u,z),u)|Accessible(z2,u)|Accessible(z3,u)|Destination(x,y). 14 [] -Destination(amountain46,aconvoy). 15 [] Convoy(aconvoy). 16 [] Yar(aconvoy). 17 [] PresentLocation(aconvoy,aconvoylocation). 19 [] Accessible(amountain46,aconvoylocation). 20 [] WindAccessible(amilbase33,amountain46). 21 [] Bioagents(aconvoy). 22 [] USBase(amilbase33). 23 [hyper,16,1] Terrorists(aconvoy). 24 [hyper,20,2,22,21,23] AttackPosition(amountain46,aconvoy,amilbase33). 25 [hyper,24,13,15,23,17,19,unit_del,14] Accessible($f1(amountain46,aconvoy,aconvoylocation,amilbase33),aconvoylocation)|Accessible(z2,aconvoylocation)|Accessible(z3,aconvoylocation). 26 [hyper,24,12,15,23,17,19,unit_del,14] Accessible($f1(amountain46,aconvoy,aconvoylocation,amilbase33),aconvoylocation)|Accessible(z2,aconvoylocation)|Camp(z3,aconvoy). 27 [hyper,24,11,15,23,17,19,unit_del,14] Accessible($f1(amountain46,aconvoy,aconvoylocation,amilbase33),aconvoylocation)|Village(z2,aconvoy)|Accessible(z3,aconvoylocation). 28 [hyper,24,10,15,23,17,19,unit_del,14] Accessible($f1(amountain46,aconvoy,aconvoylocation,amilbase33),aconvoylocation)|Village(z2,aconvoy)|Camp(z3,aconvoy). 29 [hyper,24,9,15,23,17,19,unit_del,14] CaveSystem($f1(amountain46,aconvoy,aconvoylocation,amilbase33),aconvoy)|Accessible(z2,aconvoylocation)|Accessible(z3,aconvoylocation). 30 [hyper,24,8,15,23,17,19,unit_del,14] CaveSystem($f1(amountain46,aconvoy,aconvoylocation,amilbase33),aconvoy)|Accessible(z2,aconvoylocation)|Camp(z3,aconvoy). 31 [hyper,24,7,15,23,17,19,unit_del,14] CaveSystem($f1(amountain46,aconvoy,aconvoylocation,amilbase33),aconvoy)|Village(z2,aconvoy)|Accessible(z3,aconvoylocation). 32 [hyper,24,6,15,23,17,19,unit_del,14] CaveSystem($f1(amountain46,aconvoy,aconvoylocation,amilbase33),aconvoy)|Village(z2,aconvoy)|Camp(z3,aconvoy). 33 [hyper,26,4,25,factor_simp,factor_simp] Accessible($f1(amountain46,aconvoy,aconvoylocation,amilbase33),aconvoylocation)|Accessible(z2,aconvoylocation). 34 [hyper,27,5,33,factor_simp] Accessible($f1(amountain46,aconvoy,aconvoylocation,amilbase33),aconvoylocation)|Accessible(z3,aconvoylocation). 35 [hyper,28,5,33,factor_simp] Accessible($f1(amountain46,aconvoy,aconvoylocation,amilbase33),aconvoylocation)|Camp(z3,aconvoy). 36 [hyper,35,4,34,factor_simp] Accessible($f1(amountain46,aconvoy,aconvoylocation,amilbase33),aconvoylocation). 37 [hyper,29,3,36] Accessible(z2,aconvoylocation)|Accessible(z3,aconvoylocation). 38 [hyper,30,3,36] Accessible(z2,aconvoylocation)|Camp(z3,aconvoy). 39 [hyper,38,4,37,factor_simp] Accessible(z2,aconvoylocation). 40 [hyper,31,5,39] CaveSystem($f1(amountain46,aconvoy,aconvoylocation,amilbase33),aconvoy)|Accessible(z3,aconvoylocation). 41 [hyper,40,3,36] Accessible(z3,aconvoylocation). 42 [hyper,32,5,39] CaveSystem($f1(amountain46,aconvoy,aconvoylocation,amilbase33),aconvoy)|Camp(z3,aconvoy). 43 [hyper,42,3,36] Camp(z3,aconvoy). 44 [hyper,43,4,41] $F. ------------ end of proof ------------- Shows that mountain46 is convoy’s destination

53. Resolution  Athena  English Automatic generation of proofs in natural language, roughly in the same style that one encounters in rigorous proofs appearing in mathematical texts. Coupled with the automatic generation of counter- examples (in the form of finite models), such a feature should greatly help engineers building digital systems. Automatically generated counter-examples will help to catch bugs in the early stages of design and implementation; automatically generated proofs expressed in English will validate their design and implementation choices in later stages by demonstrating why the systems work. Athena has just proved the UNIX OS sound! A lightning-fast 6000-long proof.

54. Simple Reasoning Problem Everyone loves anyone who loves someone. Alvin loves Bill. Can you infer that everyone loves Bill? ANSWER: JUSTIFICATION:

55. (assert '(alive marc) :name 'marc-alive) (assert '(birthtime marc (date-point 1977 2 10 9 24)) :name 'marc-birthtime) (assert '(biological-mother regina marc) :name 'marc-mother) (assert '(biological-father josephjr marc) :name 'marc-father) (assert '(sister christine marc) :name 'marc-sister) (declare-predicate-symbol 'parent 2 :falsify-code 'irreflexivity-falsifier) (assert '(forall (?person) (not (parent ?person ?person))) :name 'parent-irreflexive) (assert '(forall (?person) (iff (parent ?person) (exists (?person1) (parent ?person ?person1)))) :name 'parent-unary-defintion) (assert '(forall (?person1 ?person2) (iff (parent ?person1 ?person2) (child ?person2 ?person1))) :name 'parent-child-inverse) (assert '(forall (?person1 ?person2) (iff (parent ?person1 ?person2) (or (biological-parent ?person1 ?person2) (adoptive-parent ?person1 ?person2) (step-parent ?person1 ?person2) (foster-parent ?person1 ?person2)))) :name 'parent-subdivision) (assert '(forall (?person) (iff (mother ?person) (exists (?person1) (mother ?person ?person1)))) :name 'mother-unary-defintion) (assert '(forall (?person1 ?person2) (iff (mother ?person1 ?person2) (and (parent ?person1 ?person2) (female ?person1)))) :name 'mother-binary-defintion)) (assert '(forall (?person) (exists (?time-interval) (lifespan ?person ?time-interval))) :name 'all-persons-have-lifespan) (assert '(forall (?person ?time-point ?time-interval) (iff (alive-at-time ?person ?time-point) (and (lifespan ?person ?time-interval) (temporally-intersects ?time-interval ?time-point)))) :name 'define-alive-at-time-point) (assert '(forall (?person) (iff (alive ?person) (alive-at-time ?person now))) :name 'define-alive) vMEM Initially, a Q/A theorem proving-based system in which Questions will be answered by deducing Answers from the knowledge base corresponding to vMEM. This knowledge base will be constructed in keeping with the construction of “deep” characters in narrative.