SIGGI-AACS

1. SIGGI-AACS Smart Databases for smart users

2. etymology • SIGGI is a neural agent; name comes from “sigmoid” as in curve function and “Sigmund” as in Freudian • A: Archaeological • A: Auto- • C: classification • S: System

3. Freudian Interface • SIGGI is a neural agent for our AI interface • SIGGI is a surrogate for the archaeological expert • SIGGI can learn with training • SIGGI’s ideas on classification change with new teachers and new information • SIGGI will become an advisor, remain a surrogate, and perhaps, become a teacher some day

4. Smart databases are dynamic • Design goals: - create interoperable databases - allow variable views of scientific information to be supported, while ensuring electronic accessibility - allow the database to be self-correcting - develop standard tools that lead to building national infrastructure - smart databases will have analytical tools that use database information to make predictions

5. Data building • Archaeological databases have - complex data types - data and data types and interpretations are constantly changing • 2-fold data complexity: - archaeologists need to organize the same data in different ways - archaeologists may differ on what are the requisite or important data

6. SIGGI – AACS Project Objectives • Establish a dynamic data model with smart user interfaces for large data sets • Develop these models working closely with federal and state managers, tribal groups, researchers and public users • Develop a smart neural agent for systematic classification of archaeological diagnostics • Demonstrate that federal and state databases can be held securely • Salvage old and fragmented databases into the new powerful and extensible data structure • Store artifact data as live digital images • Train archaeologists in use of the database • Disseminate results in meetings and publications

7. Making Thoughtful Agents

8. How Does SIGGI Think?

9. Expert Knowledge • Obtain information from domain expert • Classify methods by interactions with domain experts • Classify by types of information elicited from domain experts

10. SIGGI Prototype • Domain Expert: Lohse (1985) Rufus Wood Lake projectile point chronology used. Large collection with established provenience and radiocarbon dates.

11. Basic Training for SIGGI • Lohse classification chosen because it was explicitly based on established types, • had clean provenience information, • a suite of radiocarbon dates, • a clear analytical framework, • and was statistically driven.

12. Rufus Wood Lake Projectile Point Chronology Modal types - descriptive - historical

13. Lanceolate Forms Defined Metrically

14. Lanceolate Forms Defined Conceptually in Dimensional Space

15. Lanceolate Forms Distinguished

16. Triangular Forms Defined Metrically

17. Triangular Forms Defined Conceptually in Dimensional Space

18. BASIC TRAINING

19. EXPERT KNOWLEDGE BASED SYSTEMS: ELEMENTS • KNOWLEDGE DATABASE • HUMAN DESIGNER/USER • DIAGNOSTIC: DESIGN • INTELLIGENT BEHAVIORS < CONTROLS > - PERTINENT KNOWLEDGE - SEQUENTIAL DEDUCTIONS AND TRANSFORMS

20. EXPERT KNOWLEDGE BASED SYSTEMS: KEYS • KNOWLEDGE ACQUISITION • KNOWLEDGE REPRESENTATION • KNOWLEDGE UTILISATION

21. THE DATABASE

22. Database Structure

23. Recording Outlines: Inputs

24. USER Access and Security

25. Reality Bytes: We Can’t Include Them All • Sampling an inherent issue • Ideal is unreachable: impossible because the number of possible observations is infinite • Must use precedent: the Traditional • Modern technology merely makes the issues of sampling and bias more evident

26. Content-based Image Retrieval • Types of queries: Level 1: retrieval of “primitive features” (color, texture, shape, spatial location) Level 2: retrieval by derived or logical features (by type or by object) Level 3: retrieval by abstract attributes (interpretation of forms)

27. Data Retrieval: Different Questions Type assignment of fragments Type descriptions

28. USER INTERFACES

29. SIGGI USER SCREEN

30. LOADING IMAGES

31. IMAGE ANALYSIS

32. PROBABILITY ASSIGNMENT

33. To Do’s • Obtain larger samples of Plateau projectile points from known contexts • Bring archaeological experts to train and interact with SIGGI • Demonstrate research potential of AI neural networks in refining typological issues

34. Suggested References E.S. Lohse 1985 Rufus Woods Lake Projectile Point Chronology. In S.K. Campbell (ed.), Summary of Results, Chief Joseph Dam Cultural Resources Project, Washington, pp.317-364. Seattle: Office of Public Archaeology. 1994 The Southeastern Idaho Prehistoric Sequence. Northwest Anthropological Research Notes 28(2):135-156. 1995 Northern Intermountain West Projectile Point Chronology. Tebiwa 25:3-51. • 2003 Automated Classification of Stone Projectile Points in a Neural Network, with C. Schou, A. Strickland, D. Sammons and R. Schlader. Paper to be published in Proceedings of the 2003 Computer Applications in Archaeology Conference, BAR International Series, W. Borner (ed.). • 2003 “Automated Classification a Neural Network: Information Data Archives,” with C. Schou, A. Strickland, D. Sammons, and R. Schlader. Paper presented at the Fifith World Archaeological Congress, Catholic University, Washington, D.C. In “Managing Archaeological Resources: Advancing Access to Digital Data: Strategies for Preserving Archaeological Digital Records,” M.S. Carroll, A. Simon and A. Prinke (co-organizers). http://imnh.isu.edu/wac5/