Detailed Automated Comparisons of Distinctive Projectile Point Type Sequences in the Northern Interm

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Detailed Automated Comparisons of Distinctive
Projectile Point Type Sequences in the Northern
Intermountain West

• by E.S. Lohse, Corey Schou, R. Schlader and D.
  Sammons Informatics Research Institute, Idaho
  State University

Paper presented at 63rd Annual Plains
Conference Edmonton, Alberta, Canada Oct 19-23,
2005.
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Expert System SIGGI-AACS

• SIGGI is
• a Neural Agent
• who Learns Rules
• and Thinks Creatively

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SIGGIs Thoughts

• To train a neural agent in an AI system we must
  make implicit referential systems explicit
• We extract expert knowledge and create
  hierarchical structures or decision trees
• Siggi learns to think like an archaeologist
• But Siggi has reference to much more
  authenticated data

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SIGGI Prototype

• Domain Expert Lohse (1985), Columbia Plateau
  cultural area
• Rufus Wood Lake projectile point chronology used.
  
• Large collection with established provenience and
  radiocarbon dates.

• 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.

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Expert Training Lohse 1985

• SIGGI has currently been trained to employ the
  classification system developed by Lohse (1985)
  for the Columbia Plateau
• Explicit
• Statistically based
• Authenticated data
• Community acceptance

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1st Order Rules Lanceolates

• Variable forms are reduced to abstract geometric
  ideals
• SIGGI is taught that this is important and that
  this leads to further distinctions

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1st Order Rules Triangular
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Classifying Points in Multidimensional Space

• Handles real-world variability
• Creates measurable relationships
• Defines polythetic sets that offer discrimination
  between types
• Constructs discriminant functions that
  distinguish between sets and individual specimens
• Allows continued classification and objective
  assessments of class relations as the dataset
  expands

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Series centroid plots

• 6 morphological type series
• Clear statistical separation
• Simple lanceolate and side-notched series show
  greatest separation

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Discriminant functions

• A mathematical function is a Cartesian product of
  a set by itself where each pair of the product is
  assigned a real number
• Relations are sets of ordered pairs where
  functions are sets of ordered triples
• Measured points can be placed in matrices and
  distances plotted (correlation matrices)

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Rotated standardized discriminant function
coefficients, lanceolate run (Lohse 1985)

• F1 haft length
• F2 neck width, blade width, shoulder angle,
  shoulder length
• F1 and F2 adequately explain 91 of the variation
  in lanceolate types

Table 11-6, Lohse 1985
Conclusion for lanceolates, haft length and
development of well defined shoulders, coupled
with blade width and neck width are the major
variables used to distinguish recognized
lanceolate from shouldered lanceolate types.
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Rotated standardized discriminant function
coefficients, triangular run (Lohse 1985)
Table 11-7, Lohse 1985
Conclusion for triangular points, variance in
form is more subtle than in Lanceolate types,
and the primary discriminating variables are
shoulder Angle, basal margin angle and stem size
or overall proportion, reflected in Basal width
and the ratio between neck and basal width.
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Canonical Discriminant Runs, Lohse (1985)
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Cases correctly classified, Lohse (1985)
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Rufus Wood Lake Projectile Point Chronology
Modal types - descriptive - historical
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Measurement Grids
Triangular Forms
Lanceolate Forms
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SIGGI doesnt use discriminant analysis

• SIGGI is short for use of sigmoid functions as
  part of curve fitting related to form recognition
• Sigmoid functions are often used in neural
  networks to introduce nonlinearity in the model
  and to make sure that certain signals remain
  within a specified range.
• A popular neural net element computes a linear
  combination of its input signals, and applies a
  bounded sigmoid function to the result this
  model can be seen as a "smoothed" variant of the
  classical threshold neuron.

http//www.answers.com/topic/sigmoid-function
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Curve fitting sigmoid functions
http//mathworld.wolfram.com/SigmoidFunction.html
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Actual outlines are reduced to geometric forms

• Objects defined in a descriptive system can be
  conceived as points in space, whose dimensions
  are descriptors of the objects
• - 2D space (2 measured axes elements in the
  vectors) distributions are regular, linear,
  clustered or random
• - 3D space (Euclidean 3D space 5D etc.
  coordinates constitute vectors) clusters now
  occur in multiple dimensions
• N2 clusters in the 5 empirical dimensions
  constitute 2 new abstract dimensions composed of
  multiple descriptors

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Importance of Latent Dimensions

• Data elements are transformed into latent
  dimensions in order to manipulate solutions at
  the abstract or symbolic level
• Latent dimensions can be found easily on the
  computer
• The latent dimensions are archaeological
  structures ( polythetic sets)
• Identification of latent dimensions allows
  removal of unnecessary information using Bayesian
  inference models

http//reach.ucf.edu/aln/pyle/theory.html
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Intersecting Variables
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TYPES

• RULES FOR INCLUSION OF DATA
• 1- A defined type must have a clearly proscribed
  range of variation defined quantitatively or
  qualitatively
• 2- The named type must have been recovered in
  definable archaeological contexts, and is
  isolatable in specific stratigraphic sequences

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SITES

• RULES FOR INCLUSION OF DATA
• 1- Sites must have been excavated in cultural
  stratigraphic levels and not in natural or
  arbitrary levels.
• 2- Provenience information must be available for
  all recovered artifacts that specifies cultural
  units as to stratum, feature and association.
• 3- A detailed descriptive report covering
  excavation methodology and analysis must be
  published for the site or be in the process of
  publication, and excavation notes and photographs
  must be on file at a recognized repository.

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ASSEMBLAGES

• RULES FOR INCLUSION OF DATA
• 1- An artifact distribution to qualify as a site
  activity assemblage must be defined in a discrete
  vertical and horizontal distribution associated
  with a recognizable cultural feature.
• 2- The cultural and natural stratigraphy must
  indicate that the assemblage represents a
  discrete prehistoric activity. The assemblage is
  not an analytical construct but a found context.
• 3- The assemblage indicates a discrete series of
  tasks or task-related activities. The assemblage
  is not an amalgam of activities over an extended
  period of time reflecting different seasons of
  site use nor different uses in different years.

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TYPES AND ASSEMBLAGES

• Assemblage Windust
• a composite
• Types Windust, Cascade and Cold Springs S-n
• Windust and Cascade
• are distinctive to region (cultures)
• Large Side-n non-
• distinctive as regional
• indicator ( associated
• temporal marker)

Ames et al. 1998 reproduced from Leonhardy and
Rice 1970
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Inspection of Interstices

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Plateau Arche-Types
Rabbit Island
Cascade
Windust
Lind Coulee
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Plateau Archetype Schema
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Common Structures
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Middle Archaic C-n Forms
How to identify common and unique types?
Columbia Plateau these are Columbia C-n, large
and small Plains these are Pelican
Lake specimens Basin these are Elko
C-n specimens
http//www.abheritage.ca/alberta/archaeology/i_pel
ican.html
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Late Archaic S-n Forms
How to identify common and unique types?
Columbia Plateau these are Plateau S-n Plains
these are Avonlea specimens Basin these are
Desert S-n specimens
http//lithiccastinglab.com/gallery-pages/avonleag
rouplarge.htm
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SIGGI as a user-defined smart interface
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Loading images
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Defining outlines eroding
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Simple outlines
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Probabilities of membership morphological
categories
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Probabilities of membership Historical types
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To Do

• Obtain information from domain experts
• Columbia Plateau
• Northwestern Plains
• Great Basin
• Classify methods by interactions with domain
  experts
• Classify by types of information elicited from
  domain experts

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Expert Interactions 1

• Interviews (concept mapping, cognitive structure
  analysis, data flow models, task action mapping)
• Case Studies (context-driven)
• Protocols (actions and mental processes)
• Critiquing

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Expert Interactions 2

• Role Playing
• Simulations
• Prototyping (expert evals, iterative system
  evals, rapid prototyping, storyboarding)
• Teach-back
• Basic observation

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Need for authenticity

• Only data that can be verified will be entered
  into the database
• used to train SIGGI
• used to test SIGGI
• Explicit rules for inclusion can be constructed
• sites to include
• assemblages to include

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Conclusions

• SIGGI was developed as
• an explicit classification system based on
  accepted typologies
• a smart user interface to
• manipulate large relational databases
• explore the nature of archaeological
  classification
• SIGGI as a prototype is smart enough to
• classify specimens into general accepted types
• begin explicit comparisons between cultural areas
• begin knowledge elicitation experiments with
  archaeological experts

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Suggested References

• Lohse, E.S., D. Sammons, C. Schou, A. Strickland,
  and R. Schlader, 2005, Developing Archaeological
  Informatics A Proposed Agenda for the Next
  FiveYears. Paper to be published in Proceedings
  of the 2004 Computer Applications in Archaeology
  Conference, BAR International Series, N.
  Niccolucci (ed.).
• Lohse, E.S., C. Schou, A. Strickland, D. Sammons
  and R. Schlader, 2004, Automated Classification
  of Stone Projectile Points in a Neural Network,
  in Magistrat der Stadt Wien Referat Ulturelles
  Erbe Stadarchaeologie Wien (eds.), Enter the
  Past The E-Way into the four Dimensions of
  Cultural Heritage, BAR International Series 1227,
  pp. 431-433. Oxford.
• Lohse, E.S., 1995, Northern Intermountain West
  Projectile Point Chronology. Tebiwa 25(1) 3-51.
• Lohse, E.S., 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. Final Report to
  the U.S. Army Corps of Engineers, Seattle
  District. Office of Public Archaeology,
  University of Washington, Seattle.

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Informatics Research Institute

• Center for Innovative Technology In Archeological
  Informatics CITI-AI http//iri.isu.edu/AIRC.htm