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Charles Taylor - Biology Travis Collier Yoosook Lee Yuan Yao Ed Stabler - Linguistics

Language and Biology Group. Charles Taylor - Biology Travis Collier Yoosook Lee Yuan Yao Ed Stabler - Linguistics Greg Kobele Jason Riggle. To/from humans. “Explicit” level: well-formed, explicit models. Are there or were there objects there? What kind were they?

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Charles Taylor - Biology Travis Collier Yoosook Lee Yuan Yao Ed Stabler - Linguistics

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  1. Language and Biology Group Charles Taylor - Biology Travis Collier Yoosook Lee Yuan Yao Ed Stabler - Linguistics Greg Kobele Jason Riggle

  2. To/from humans

  3. “Explicit” level:well-formed, explicit models • Are there or were there objects there? • What kind were they? • How many were there? • What did they do?

  4. Requirements at the Implicit Stage Robust changing environments/agents Wrong information noisy messages Adaptive unanticipated sources, events form new concepts different languages Self-configuring changing situations, goals

  5. Outline • Solution overview • Partial solution - Evolving language • Partial solution- Intrusion detection • Formal Analysis • Expressing knowledge with logic • Creating and learning language syntax • Semantics • Grounding problem • Passing D-structures

  6. External World Internal Representation Logical Representation Decisions about what/whom to communicate Internal Representation Logical Representation Decisions about what/whom to communicate Agent Language English-like Language English-like Language Agent Language Humans Humans

  7. Compression aids in generalization. Compression distills experience into a schema or model “This compressed form can be succinct, right, approximately correct or even wrong, but it can be useful if it can be used to generalize to situations different from previously encountered”. - Gell-Man

  8. An example of compression: y = mx + b

  9. Regular Language(Q, S , d, qo,F) Q = set of all states (finite) S = input alphabet (finite) qo = initial state F = set of final states d = transition function (“rewrite rules”) (Q x d)  Q

  10. Example: Rewrite Grammar S S  NP VP NP  D N NP  D VP  V VP  V NP V  loves V  eats D  David D  Mary N  dog D  the NP VP D V NP Mary loves D David

  11. Minimum Description Length (MDL) Algorithm - Rissanen & Ristad MDL-length = grammar-encoding-length + data-encoding-length Grammar-encoding-length (GEL) the cost of the generalization Data-encoding-length (DEL) the cost of the compression

  12. Principle of Compression Combine grammatical equivalents S4 Jane Amy S5 S S1 S2 Mary likes Caitlin S6 Jane Amy S S3 S1 S2 Caitlin Mary likes

  13. 3) Languages become more smooth?

  14. Evolution of Language with semantics (Kirby) Loves (John, Mary) xxy zzy rrx Loves (Bill, Mary) xxy aab rrx Hits (Bill, John) mmn aab zzy aab Bill zzy John rrx Mary xxy Loves etc.

  15. External World Internal Representation Logical Representation Decisions about what/whom to communicate Internal Representation Logical Representation Decisions about what/whom to communicate Agent Language English-like Language English-like Language Agent Language Humans Humans

  16. Intrusion Detection Methods • Specification-based methods ? (x)[write(x, kernel)] • Pattern Matching • signature of “red code” worm • (could be specification-based - buffer overflow) • Anomaly Detection • Scan many ports in short time • analogous to parts of our problem • unanticipated changes in the system

  17. Local Internal Events • start (Subject Program EventNo Tstamp) • chmod (Subject File Fpermissions EventNo Tstamp) • open (Subject File Mode EventNo Tstamp) • exec (Subject File Mode EventNo Tstamp) • fork (Subject NewPID EventNo Tstamp)

  18. Computer -linux External World 1. Trace of activity 2. C++ objects - each file -each process Internal Representation Logical Representation Decisions about what/whom to communicate 3. Prolog Environment - only “interesting” parts, innate, human told, deduced English-like Language Humans

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