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Is it Possible to Build Dramatically Compelling Interactive Digital Entertainment? v2

Is it Possible to Build Dramatically Compelling Interactive Digital Entertainment? v2. Games?. Selmer Bringsjord Director, Minds & Machines Laboratory Prof of Logic and Cognitive Science Dept. of Philosophy, Psychology & Cognitive Science Department of Computer Science

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Is it Possible to Build Dramatically Compelling Interactive Digital Entertainment? v2

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  1. Is it Possible to BuildDramatically Compelling Interactive Digital Entertainment? v2 Games? 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

  2. Is it Possible to BuildDramatically 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 BuildDramatically 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?

  7. Unfortunately…

  8. 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

  9. Autonomous AI in The Matrix

  10. Characters Must Be Intelligent Agents

  11. 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 returnaction

  12. The Wumpus World

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

  14. 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

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

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

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

  18. 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.

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

  20. Is the Set of All “A”s Countable?

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

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

  23. 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?…

  24. The Retreat to Grids

  25. Ten Human-Designed Gridfonts

  26. 1500 “A”s are Possible

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

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

  29. But my argument indicatesthat for this dream to becomereality will require somepreternaturally clever engineering.

  30. Toward Mendacity • x tells lie p to y iff • p is false; • x knows that p is false;

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

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

  33. Toward Mendacity • x tells lie p to y iff • x (in some technical communicative sense) tells yp; • (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!

  34. Toward Mendacity • x tells lie p to y iff • x (in some technical communicative sense) tells yp; • (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.

  35. Toward Mendacity • x tells lie p to y iff • x (in some technical communicative sense) tells yp; • (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

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