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

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 English-like Language Humans Agent Language Internal Representation Logical Representation Decisions about what/whom to communicate English-likeLanguage Agent Language Language 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, , , q o,F) Q = set of all states (finite)  input alphabet (finite) q o = initial state F = set of final states  = transition function (“rewrite rules”) (Q x  )  Q

10. Example: Rewrite Grammar 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 S NP VP D V NP Mary loves D David David

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

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

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 English-like Language Humans Agent Language Internal Representation Logical Representation Decisions about what/whom to communicate English-likeLanguage Agent Language Language 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)

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