In the process of this work, we will develop various automated tools for building Bayes nets from data and from expert opinions; design a Bayes net to probabilistic database interface; contribute several benchmark examples to the Bayes net planning community, and possibly develop useful and useable tools to support academic advising.

Mathematical Models

Advising as a BN

Why This Is Important

Research Projects

We intend to build mathematical models of this process. They can never replace the human interaction of a faculty advisor, but could supplement the advising process both by generating an initial set of advice and as a tool for comparing alternative plans.

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**Dynamic Bayes nets** are a potentially compact representation
of Markov decision processes.
Each state of the system is described by a vector of values
called *fluents*.
(Note that if each of *n* fluents is two-valued,
then the system has *2^n* states.)
Actions are described by the effect they have on each fluent
by means of two data structures.
They are a dependency graph and a set of functions encoded
as conditional probability tables, decision trees, arithmetic
decision diagrams, or in some other data structure.

The dependency graph is a directed acyclic graph with nodes
*{v_1,...,v_n}* and *{v_1',...,v_n'}*.
The first set of nodes represents the state
at time *t*, the second at time *t+1*.
The edges are from the first set of nodes to the second (asynchronous)
or within the second set (synchronous). The value of the
*k^{th}* fluent at time *t_1* under action *a*
depends probabilistically on the
values of the predecessors of *v_k'* in this graph. The
probabilities are spelled out, for each action, in the corresponding
data structure for *v_k'* and *a*.

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In order to model a transcript, we will include fluents for each possible course, where the fluents can take values from the possible grade set, including Pass, Fail, Withdrawn, and NotTaken; values will also indicate how recently the course was taken. (This remains a finite set of possible values, as courses "expire" after a fixed time.) There will be additional fluents added as needed, such as AP exams, "mathematical maturity" and "writing ability," and GRE or ACT and TOEFL scores.

Note that the initial model will be for the Masters program for the Computer Science Department. This will be considerably easier to implement that a general undergraduate advisor, both because of the magnitudes of the respective course offerings and because it will be easier to gather the information needed to design the conditional probability structures.

Later implementations will take into account a variety of constraints, including timing constraints (taking into account the timetable for each semester) and student preferences, as well as secondary optimizations such as grade point averages.

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Because real advising takes into account a wide variety of constraints (on scheduling, topic and instructor preferences, etc.), this system can also be used to model constrained optimization problems and to test related algorithms.

There are also clear benefits to having a working system, both for testing advising and for testing human advisors' assumptions about course dependencies.

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- Research and choose a DBN construction kit
- Build web-based knowledge elicitation tools to elicit probabilities
and stochastic dependency data from advisors
*In progress* - Design XML interfaces for integrating structure from elicited
and discovered data
*In progress* - Design and implement a semi-structured probabilistic database
*In progress* - Build a Bayes net implementation that is compatible with the
semi-structured probabilitic database
*AVAILABLE* -
Design and implement a parser for downloading course information and
graduation requirements from the web
*AVAILABLE* -
Design and implement knowledge-discovery algorithms for working with
the anonymous transcript data, given the elicited BN structure
*AVAILABLE* - Investigate and choose conflict-resolution strategies for
knowledge/data fusion (combining possibly conflicting expert opinions
and discovered knowledge)
*AVAILABLE* -
**Domain-specific knowledge acquisition:**- Design the initial domain (CS MS program)
*In progress* - Elicit expert opinions
*In progress* - Apply knowledge and data fusion to build model

- Design the initial domain (CS MS program)
- Research, design, and implement a policy evaluator for very large DBNs
*AVAILABLE* - Research, develop, implement, and test algorithms and heuristics
for policy finding with very large DBNs
*AVAILABLE* -
Research, develop, implement, and test heuristics for approximate
state aggregation in BNs
*In progress*

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Devan Desai,

Judy Goldsmith,

Meesoon Han,

Carol Hannahs,

Sean Hawkes,

Ryan Hunt,

Jan Pearce

John Pickens,

Jaleh Rezaie,

Brett Young

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