Expert Systems: Instructional Materials


Lie on the Fly: Strategic Voting in an Iterative Preference Elicitation Process

arXiv.org Artificial Intelligence

A voting center is in charge of collecting and aggregating voter preferences. In an iterative process, the center sends comparison queries to voters, requesting them to submit their preference between two items. Voters might discuss the candidates among themselves, figuring out during the elicitation process which candidates stand a chance of winning and which do not. Consequently, strategic voters might attempt to manipulate by deviating from their true preferences and instead submit a different response in order to attempt to maximize their profit. We provide a practical algorithm for strategic voters which computes the best manipulative vote and maximizes the voter's selfish outcome when such a vote exists. We also provide a careful voting center which is aware of the possible manipulations and avoids manipulative queries when possible. In an empirical study on four real-world domains, we show that in practice manipulation occurs in a low percentage of settings and has a low impact on the final outcome. The careful voting center reduces manipulation even further, thus allowing for a non-distorted group decision process to take place. We thus provide a core technology study of a voting process that can be adopted in opinion or information aggregation systems and in crowdsourcing applications, e.g., peer grading in Massive Open Online Courses (MOOCs).


A Graduate-Level Expert Systems Course

AI Magazine

The course size is limited to 20. It is run as a 14-week course, with one 3-hour class per week. One goal of the course is to examine a number of expert, knowledgebased, problem-solving systems, looking at each system in some depth. Another important goal is to make comparisons across systems in a domain-independent way. An attempt is made to relate systems by their problem-solving capabilities rather than merely by the AI techniques used.


Medical Decision Support

AITopics Original Links

This course presents the main concepts of decision analysis, artificial intelligence, and predictive model construction and evaluation in the specific context of medical applications. The advantages and disadvantages of using these methods in real-world systems are emphasized, while students gain hands-on experience with application specific methods. The technical focus of the course includes decision analysis, knowledge-based systems (qualitative and quantitative), learning systems (including logistic regression, classification trees, neural networks), and techniques to evaluate the performance of such systems.


"Paradigms of AI Programming" in Python

AAAI Conferences

Norvig’s (1992) Paradigms of AI Programming is an important book for learning about AI programming. However, the book uses Common Lisp as the programming language, which is less popular now than in 1992. Thus, we have translated many classical AI programs described in the book into Python, a more commonly used language. We have also documented the programs and offered them as a resource in a course on knowledge-based AI.


Toward a Category Theory Design of Ontological Knowledge Bases

arXiv.org Artificial Intelligence

I discuss (ontologies_and_ontological_knowledge_bases / formal_methods_and_theories) duality and its category theory extensions as a step toward a solution to Knowledge-Based Systems Theory. In particular I focus on the example of the design of elements of ontologies and ontological knowledge bases of next three electronic courses: Foundations of Research Activities, Virtual Modeling of Complex Systems and Introduction to String Theory.


A Business-Rules Approach for Departmental Advising

AAAI Conferences

The advising process plays an essential role in the success of a student's academic life, and should include mentoring and counseling offered by faculty advisors. Unfortunately, the limited advising time available is often spent on routine tasks such as course advising and approvals. In addition, faculty advisors spend a large proportion of advising time answering the same questions for multiple students. Nontraditional students and distance education make it more difficult for student and faculty advisor to find an agreeable time to meet. While universities have implemented webbased advisor systems for routine tasks, most of these systems make static, non-personalized materials such as catalogs available and offer email communication between student and advisor. Moving beyond this approach, we seek to provide a means by which a student may engage in interactive dialogues with a "Virtual Advisor" that possesses current, reliable knowledge and offers personalized advice. VAT (Virtual Advisor Technology), is an intelligent knowledge-based system being developed as a multi-university, multi-departmental team design project. The intent of this online advising system is to assist students in planning semester schedules and degree plans. VAT uses rule-based reasoning to generate degree plans, check scheduling conflicts, and recommend personalized advice based on student preferences.


FS94-05-019.pdf

AAAI Conferences

Priority queues A*, admissibility, monotonicity, and informedness Iterative deepening A* Beam search Two-person games Mini-Max and alpha-beta 70 Weeks 7 & 8: Architectures for AI Problem Solving (L&S, ch5) Recursive specification for queues, stacks, and priority queues The production system The blackboard Planning and Triangle Tables Weeks 9 & 10: PROLOG The PROLOG environment Relational specifications and rule based constraints Abstract data types in PROLOG Graph search with the production system A PROLOG planner Weeks 11 & 12: The Rule Based Exvert System (L&S, ch 8) Production system based search Rule stacks and "why" queries, proof trees and "how" queries Models of inductive reasoning The Stanford Certainty Factor algebra Knowledge engineering Tieresias, a knowledge based editor Week 13: Building a Rule Based Expert System in PROLOG (L&S, ch 13) Meta-predicates in PROLOG The role of a meta-interpreter: PROLOG in PROLOG Rule-stacks, proof-trees, and certainty factor algebras in PROLOG Exshell, a back-chaining rule interpreter in PROLOG Week 14: Introduction of Structured AI Representational Schemes (L&S, ch


Development and Deployment of a Disciple Agent for Center of Gravity Analysis

AAAI Conferences

This paper presents new significant advances in the Disciple approach for building knowledge-based systems by subject matter experts. It describes the innovative application of this approach to the development of an agent for the analysis of strategic centers of gravity in military conflicts. This application has been deployed in several courses at the US Army War College, and its use has been evaluated. The presented results are those of a multifaceted research and development effort that synergistically integrates research in Artificial Intelligence, Center of Gravity analysis, and practical deployment of an agent into Education.


Development and Deployment of a Disciple Agent for Center of Gravity Analysis

AAAI Conferences

This paper presents new significant advances in the Disciple approach for building knowledge-based systems by subject matter experts. It describes the innovative application of this approach to the development of an agent for the analysis of strategic centers of gravity in military conflicts. This application has been deployed in several courses at the US Army War College, and its use has been evaluated. The presented results are those of a multifaceted research and development effort that synergistically integrates research in Artificial Intelligence, Center of Gravity analysis, and practical deployment of an agent into Education.


Backward Model Tracing: An Explanation-Based Approach for Reconstructing Student Reasoning

AAAI Conferences

An original methodology, called backward model tracing to model student performance which features a profitable integration of the bug collection and bug construction techniques is presented. This methodology has been used for building the modelling module of a new version of ET (English Tutor), an ITS aimed at supporting the learning of the English verb system. Backward model tracing is based on the idea of analyzing the reasoning process of the student by reconstructing, step by step and in reverse order, the chain of reasoning (s)he has followed in giving his/her answer. In order to do this, both correct domain specific knowledge and a catalogue of stereotyped errors (mahules) are utilized. When the system is unable to explain the student behavior by exploiting its previous knowledge, new malrules are generated dynamically, by utilizing explanation-based learning techniques. The overall process is based on a deep modelling of the student problem solving and the discrimination among possible explicative hypotheses about the reasons underlying the student behavior is carried on nonmonotonically through a truth maintenance system. The proposed approach has been fully implemented in a student modelling module developed in PROLOG.