1. Is it Possible to Build Dramatically Compelling Interactive Digital Entertainment? v2 Selmer Bringsjord Director, Minds & Machines Laboratory Prof of Logic and Cognitive Science Dept. of Philosophy, Psychology & Cognitive Science Department of Computer Science Rensselaer Polytechnic Institute (RPI) Troy NY 12180 USA Chief Scientist, Document Development Corporation 1223 Peoples Ave. Troy NY 12180 USA selmer@rpi.edu http://www.rpi.edu/~brings Games?

2. Is it Possible to Build Dramatically Compelling Interactive Digital Entertainment? Selmer Bringsjord Director, Minds & Machines Laboratory Prof of Logic, Cognitive Science, & Computer Science Dept. of Philosophy, Psychology & Cognitive Science Department of Computer Science Rensselaer Polytechnic Institute (RPI) Troy NY 12180 USA Chief Scientist, Document Development Corporation 1223 Peoples Ave. Troy NY 12180 USA selmer@rpi.edu http://www.rpi.edu/~brings

3. Is it Possible to Build Dramatically Compelling Interactive Digital Entertainment? Selmer Bringsjord Director, Minds & Machines Laboratory Prof of Logic, Cognitive Science, & Computer Science Dept. of Philosophy, Psychology & Cognitive Science Department of Computer Science Rensselaer Polytechnic Institute (RPI) Troy NY 12180 USA Chief Scientist, Document Development Corporation 1223 Peoples Ave. Troy NY 12180 USA selmer@rpi.edu http://www.rpi.edu/~brings

4. We Have Dramatically Compelling Digital Entertainment

5. Two Preliminary Points Mathematically-based realism about AI (and, in this case, narrative) –Solvable vs Unsolvable Problems Computers aren’t finite diagrams or finite state automata, but rather LBAs or Turing machines, and as such are impotent in the face of an infinite number of problems There’s no free lunch –Automated learning isn’t going to give us great NPC’s –Laird –Ergo, Logic!

6. Logical Systems: Which for DCIDE? NameAlphabetGrammarProof Theory SemanticsMetatheory L PC Propositional Calculus p, q, r, … and truth- functional connectives EasyFitch-style and natural deduction, resolution, etc. Truth tables!Sound, complete, compact, decidable L I First-Order Logic Add variables x, y, … and   EasyFitch-style and natural deduction, resolution, e.g. Structures and interpretations Sound, complete, compact, undecidable L PML Add “box” and “diamond” for necessity and possibility Wffs created by prefixing new operators to wffs Add necessitation, etc. Possible worlds Similar to L PC L II New variables for predicates Pretty obviousNew adapt quantifier rules Quantification over subsets in domain allowed Sound but not complete

7. Unfortunately… NameAlphabetGrammarProof Theory SemanticsMetatheory L PC Propositional Calculus p, q, r, … and truth- functional connectives EasyFitch-style and natural deduction, resolution, etc. Truth tables!Sound, complete, compact, decidable L I First-Order Logic Add variables x, y, … and   EasyFitch-style and natural deduction, resolution, e.g. Structures and interpretations Sound, complete, compact, undecidable L PML Add “box” and “diamond” for necessity and possibility Wffs created by prefixing new operators to wffs Add necessitation, etc. Possible worlds Similar to L PC L II New variables for predicates Pretty obviousNew adapt quantifier rules Quantification over subsets in domain allowed Sound but not complete

10. Some Key Challenges Formalizing Literary Themes –For me it’s been betrayal –Coming: mendacity –“Selmer, we want X in our game.” Well, I need some serious money for that. Story Mastery –Without it, hack-and-slash, at best –The Bates experiment –Fortunes to be made here Building Robust Autonomous Characters Personalization Mendacity

11. Autonomous AI in The Matrix

12. Characters Must Be Intelligent Agents

13. Generic Knowledge-Based Agent (smarter than what appear in nearly all games, including, e.g., Hitman) function KB-Agent(percept) returns an action inputs: p, a percept static: KB, a knowledge base t, a counter, initially 0 Tell(KB, Make-Percept-Sentence(percept, t)) action  Ask(KB, Make-Action-Query(t)) Tell(KB, Make-Action-Sentence(action, t)) t  t + 1 return action

14. The Wumpus World

15. We Build Agents Like This in the Minds & Machines Laboratory

16. But Personhood Involves… Ability to communicate in a language Autonomy (“free will”) Creativity Phenomenal consciousness (= subjective awareness, qualia, what-it’s-like-to-be-you consciousness, P-consciousness) Robust abstract reasoning today

17. Turing Test Judge What color and in what style is your hair?

18. Judge What color and in what style is your hair? Designer In the TT, it’s Really Judge vs. Designer I can handle tha- uh, it can handle that one.

19. Judge (= Designer) Designer (= Judge) The Lovelace Test o S How did it do that?

20. Definition of Lovelace Test Artificial agent A, designed by H, passes LT if and only if –A outputs o; –A’s outputting o is not the result of a fluke hardware error, but rather the result of processes A can repeat; –H (or someone who knows what H knows, and has H’s resources) cannot explain how A produced o by appeal to A’s architecture, knowledge-base, and core functions.

21. What Systems Fail LT? Brutus (see final chapter) Copycat (see book as well) Letter Spirit......

22. Is the Set of All “A”s Countable?

23. The Original Dream Percepts: ?Actions: Design remaining letters A B C D E F … Z

24. Letter Spirit System as an Intelligent Agent Percepts: seed letters Actions: Design remaining letters A B C Letter Spirit D E F … Z

25. Step #1 Digitize! –Figure X-5 Ten human-designed gridfonts (Fig X-6) 1500 A’s (Fig X-7) Okay, now how does this work?…

26. The Retreat to Grids

27. Ten Human-Designed Gridfonts

28. 1500 “A”s are Possible

29. The Argument That Worries Me 1Dramatically compelling interactive digital entertainment requires the presence in such entertainment of virtual persons, and therefore requires the presence of autonomous virtual characters. 2Autonomous virtual characters would pass the Lovelace Test. 3Autonomous virtual characters would be intelligent agents, in the technical sense of “intelligent agents” in use in AI (specifically in AIMA). 4Intelligent agents fail the Lovelace Test. Therefore: 5Dramatically compelling interactive digital entertainment isn't possible.

30. Again: I Want to Administer the Turing Test in a Digital World…

31. But my argument indicates that for this dream to become reality will require some preternaturally clever engineering.

32. Toward Mendacity x tells lie p to y iff –p is false; –x knows that p is false;

33. Toward Mendacity x tells lie p to y iff –p is false; –x knows that p is false; But where’s the communication?

34. Toward Mendacity x tells lie p to y iff –x (in some technical communicative sense) tells y p; (Using AIMA, we could invoke TELLing to another’s KB) –p is false; –x knows that p is false;

35. Toward Mendacity x tells lie p to y iff –x (in some technical communicative sense) tells y p; (Using AIMA, we could invoke TELLing to another’s KB) –p is false; –x knows that p is false; But perhaps x is being sarcastic!

36. Toward Mendacity x tells lie p to y iff –x (in some technical communicative sense) tells y p; (Using AIMA, we could invoke TELLing to another’s KB) –p is false; –x knows that p is false; –x wants y to believe p.

37. Toward Mendacity x tells lie p to y iff –x (in some technical communicative sense) tells y p; (Using AIMA, we could invoke TELLing to another’s KB) –p is false; –x knows that p is false; –x wants y to believe p. Does this do it? Back: Some Key Challenges