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Semantic Goal-Oriented Communication

Semantic Goal-Oriented Communication. Madhu Sudan Microsoft Research + MIT. Joint with Oded Goldreich ( Weizmann ) and Brendan Juba ( MIT ). TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A A A. Disclaimers. Work in progress (for ever) …

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Semantic Goal-Oriented Communication

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  1. Semantic Goal-Oriented Communication MadhuSudan Microsoft Research + MIT Joint withOdedGoldreich(Weizmann)and Brendan Juba(MIT). Semantic Communication @ UCSD TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AAAA

  2. Disclaimers Work in progress (for ever) … Feedback welcome. Interruptions welcome during the talk. … Alas, no algebra  Semantic Communication @ UCSD

  3. The Meaning of Bits Alice Channel Bob • Is this perfect communication? • What if Alice is trying to send instructions? • Aka, an algorithm • Does Bob understand the correct algorithm? • What if Alice and Bob speak in different (programming) languages? 01001011 01001011 Freeze! Bob Semantic Communication @ UCSD

  4. Miscommunication (in practice) • Exchanging (powerpoint) slides. • Don’t render identically on different laptops. • Printing on new printer. • User needs to “learn” the new printer, even though printer is quite “intelligent”. • Many such examples … • In all cases, sending bits is insufficient. • Notion of meaning … intuitively clear. • But can it be formalized? • Specifically? Generically? • While conforming to our intuition Semantic Communication @ UCSD

  5. Bits vs. their meaning • Say, Userand Serverknow different programming languages. Serverwishes to send an algorithm A to User. • A = sequence of bits … (relative to prog. language) • Bad News: Can’t be done • For every User, there exist algorithms A and A’, and Servers S and S’ such that SsendingAis indistinguishable (to User) from S’sending A’ • Good News: Need not be done. • From Bob’s perspective, ifA and A’ are indistinguishable, then they are equally useful to him. • What should be communicated? Why? Semantic Communication @ UCSD

  6. Part I: Computational Motivation Semantic Communication @ UCSD

  7. Computational Goal for Bob • Why does User want to learn algorithm? • Presumably to compute some function f (A is expected to compute this function.) • Lets focus on the function f. • Setting: • User is prob. poly time bounded. • Server is computationally unbounded, does not speak same language as User, but is “helpful”. • What kind of functions f? • E.g., uncomputable, PSPACE, NP, P? Semantic Communication @ UCSD

  8. Setup Server User f(x) = 0/1? R Ã $$$ qk q1 a1 ak Different from interactions in cryptography/security: There, User does not trustServer, while here he does not understand her. Computes P(x,R,a1,…,ak) Hopefully P(x,…) = f(x)! Semantic Communication @ UCSD

  9. Intelligence & Cooperation? • For User to have a non-trivial interaction, Server must be: • Intelligent: Capable of computing f(x). • Cooperative: Must communicate this to User. • Formally: • Server S is f-helpful if 9some (other) user U’ s.t. 8 x, starting states ¾ of the server (U’(x) $ S(¾)) outputs f(x) Semantic Communication @ UCSD

  10. Successful universal communication • Universality: Universal User U should be able to talk to any (every) f-helpful server S to compute f. • Formally: • U is f-universal, if 8f-helpful S, 8¾, 8x (U(x) $ S(¾)) = f(x) (w.h.p.) • What happens if S is not helpful? • Paranoid view ) output “f(x)” or “?” • Benign view ) Don’t care (everyone is helpful) Semantic Communication @ UCSD

  11. Main Theorems [Juba & S. ‘08] • If fis PSPACE-complete, then there exists a f-universal user who runs in probabilistic polynomial time. • Extends to checkable (“compIP”) problems • (NP Å co-NP, breaking cryptosystems) • S not helpful ) output is safe • Conversely, if there exists a f-universal user, then fis PSPACE-computable (in “compIP”) • Scope of computation by communication is limited by misunderstanding (alone). Semantic Communication @ UCSD

  12. Proofs? • Positive result: • f Є PSPACE ) membership is verifiable. • User can make hypothesis about what the Server is saying, and use membership proof to be convinced answer is right, or hypothesis is wrong. Enumerate, till hypothesis is right. • Negative result: • In the absence of proofs, sufficiently rich class of users allow arbitrary initial behavior, including erroneous ones. • (Only leads to finitely many errors …) Semantic Communication @ UCSD

  13. Implications • Communication is not unboundedly helpful  • If it were, should have been able to solve every problem (not just (PSPACE) computable ones). • But there is gain in communication: • Can solve more complex problems than on one’s own, but not every such problem. • Resolving misunderstanding? Learning Language? • Formally No! No such guarantee. • Functionally Yes! If not, how can user solve such hard problems? Semantic Communication @ UCSD

  14. Principal Criticisms Next part of talk • Solution is no good. • Enumerating hypotheses is too slow. • Approach distinguishes right/wrong; does not solve search problem. • Search problem needs new definitions to allow better efficiency. • Problem is not the right one. • Computation is not the goal of communication. Who wants to talk to a PSPACE-complete server? Semantic Communication @ UCSD

  15. Part II: Generic Goals of Communication Semantic Communication @ UCSD TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AAAA

  16. Alice Charlie Bob Dick F X F(X) Alice Charlie Bob Dick Aside: Communication? • Classical “Theory of Computing” • Issues: Time/Space on DFA? Turing machines? • Modern theory: • Issues: Reliability, Security, Privacy, Agreement? • If communication is so problematic, then why not “Just say NO!”? Semantic Communication @ UCSD

  17. Communication: Example 1 (Printing) Semantic Communication @ UCSD

  18. Communication: Ex. 2 (Computation) X f(X) Semantic Communication @ UCSD

  19. Communication: Ex. 3 (Web search) WWW Q Q P Q(WWW(P)) = ? Semantic Communication @ UCSD

  20. Communication: Ex. 4 (Intelligence?) Yes No Semantic Communication @ UCSD

  21. Aside: Modelling Computing • Classically: Turing Machine/(von Neumann) RAM. • Described most computers being built? • Modern computers: more into communication than computing. • What is the mathematical model of a communicating computer? • What is universality? Semantic Communication @ UCSD

  22. Communication: Ex. 2 (Computation) X f(X) Semantic Communication @ UCSD

  23. Generic communication problem? Should model All 4 examples! WWW Q X f(X) Yes No Q P Q(WWW(P)) = ? Semantic Communication @ UCSD

  24. Modelling User/Interacting agents User • (standard AI model) • User has state and input/output wires. • Defined by the map from current state and input signals to new state and output signals. Semantic Communication @ UCSD

  25. Generic Goal? X X X User Server • Goal = function of ? • User? – But user wishes to change actions to achieve universality! • Server? – But server also may change behaviour to be helpful! • Transcript of interaction? – How do we account for the many different languages? Semantic Communication @ UCSD

  26. Generic Goals User Server Referee/Environment • Key Idea: Introduce 3rd entity: Referee • Poses tasks to user. • Judges success. • Generic Goal specified by • Referee (just another agent) • Boolean Function determining if the state evolution of the referee reflects successful achievement of goal. • Class of users/servers. Semantic Communication @ UCSD

  27. Generic Goals User Server Referee/Environment User Server Referee/Environment Pure Control Pure Informational Semantic Communication @ UCSD

  28. Sensing & Universality • To achieve goal, User should be able to sense progress. • I.e., user should be compute a function that (possibly with some delay, errors) reflects achievement of goals. • Generalization of positive result: • Generic goals (with technical conditions) universally achievable if 9 sensing function. • Generalization of negative result: • Sensing is necessary (in one-shot goals) • (In infinite goals, If non-trivial generic goal is achieved with sufficiently rich class of helpful servers, then it is safely achieved with every server.) Semantic Communication @ UCSD

  29. Conclusions • Is there a universal communication protocol? • No! (All functions vs. PSPACE-computable functions). • But can achieve “sensible” goals universally. • But … diversity of goals may be the barrier to universality. • Goals of communication. • Should be studied more. • Suggests good heuristics for protocol design: • Server = Helpful? • User = Sensing? Semantic Communication @ UCSD

  30. Language Learning • Meaning = end effect of communication. • [Dewey 1920s, Wittgenstein 1950s] • What would make learning more efficient? • What assumptions about “language”? • How to do encapsulate it as “class” restrictions on users/servers. • What learning procedures are efficient? • Time to get back to meaningful conversation! Semantic Communication @ UCSD

  31. References • Juba & S. • ECCC TR07-084: http://eccc.uni-trier.de/report/2007/084/ • Goldreich, Juba & S. • ECCC TR09-075: http://eccc.uni-trier.de/report/2009/075/ Semantic Communication @ UCSD

  32. Thank You! Semantic Communication @ UCSD

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