Most Relevant Explanation in Bayesian Networks

AAAI Conferences

A major inference task in Bayesian networks is explaining why some variables are observed in their particular states using a set of target variables. Existing methods for solving this problem often generate explanations that are either too simple (underspecified) or too complex (overspecified). In this paper, we introduce a method called Most Relevant Explanation (MRE) which finds a partial instantiation of the target variables that maximizes the generalized Bayes factor (GBF) as the best explanation for the given evidence. Our study shows that GBF has several theoretical properties that enable MRE to automatically identify the most relevant target variables in forming its explanation. In particular, conditional Bayes factor (CBF), defined as the GBF of a new explanation conditioned on an existing explanation, provides a soft measure on the degree of relevance of the variables in the new explanation in explaining the evidence given the existing explanation. As a result, MRE is able to automatically prune less relevant variables from its explanation. We also show that CBF is able to capture well the explaining-away phenomenon that is often represented in Bayesian networks. Moreover, we define two dominance relations between the candidate solutions and use the relations to generalize MRE to find a set of top explanations that is both diverse and representative. Case studies on several benchmark diagnostic Bayesian networks show that MRE is often able to find explanatory hypotheses that are not only precise but also concise.


Most Relevant Explanation in Bayesian Networks

Journal of Artificial Intelligence Research

A major inference task in Bayesian networks is explaining why some variables are observed in their particular states using a set of target variables. Existing methods for solving this problem often generate explanations that are either too simple (underspecified) or too complex (overspecified). In this paper, we introduce a method called Most Relevant Explanation (MRE) which finds a partial instantiation of the target variables that maximizes the generalized Bayes factor (GBF) as the best explanation for the given evidence. Our study shows that GBF has several theoretical properties that enable MRE to automatically identify the most relevant target variables in forming its explanation. In particular, conditional Bayes factor (CBF), defined as the GBF of a new explanation conditioned on an existing explanation, provides a soft measure on the degree of relevance of the variables in the new explanation in explaining the evidence given the existing explanation. As a result, MRE is able to automatically prune less relevant variables from its explanation. We also show that CBF is able to capture well the explaining-away phenomenon that is often represented in Bayesian networks. Moreover, we define two dominance relations between the candidate solutions and use the relations to generalize MRE to find a set of top explanations that is both diverse and representative. Case studies on several benchmark diagnostic Bayesian networks show that MRE is often able to find explanatory hypotheses that are not only precise but also concise.


Using Virtual Patients to Train Clinical Interviewing Skills

AAAI Conferences

Virtual patients are viewed as a cost-effective alternative to standardized patients for role-play training of clinical interviewing skills. However, training studies produce mixed results. Students give high ratings to practice with virtual patients and feel more self-confident, but they show little improvement in objective skills. This confidence-competence gap matches a common cognitive illusion, in which students overestimate the effectiveness of training that is too easy. We hypothesize that cost-effective training requires virtual patients that emphasize functional and psychological fidelity over physical fidelity. We discuss 12 design decisions aimed at cost-effective training and their application in virtual patients for practicing brief intervention in alcohol abuse. Our STAR Workshop includes 3 such patients and a virtual coach. A controlled experiment evaluated STAR and compared it to an easier E-Book and no-training Control. E-Book subjects displayed the illusion, giving high ratings to their training and self-confidence, but performing no better than Control subjects on skills. STAR subjects gave high ratings to their training and self-confidence and scored better higher than E-Book or Control subjects on skills. We invite other researchers to use the underlying Imp technology to build virtual patients for their own work.


Different but Equal: Comparing User Collaboration with Digital Personal Assistants vs. Teams of Expert Agents

arXiv.org Artificial Intelligence

This work compares user collaboration with conversational personal assistants vs. teams of expert chatbots. Two studies were performed to investigate whether each approach affects accomplishment of tasks and collaboration costs. Participants interacted with two equivalent financial advice chatbot systems, one composed of a single conversational adviser and the other based on a team of four experts chatbots. Results indicated that users had different forms of experiences but were equally able to achieve their goals. Contrary to the expected, there were evidences that in the teamwork situation that users were more able to predict agent behavior better and did not have an overhead to maintain common ground, indicating similar collaboration costs. The results point towards the feasibility of either of the two approaches for user collaboration with conversational agents.


The Role of Prompting and Feedback in Facilitating Students’ Learning about Science with MetaTutor

AAAI Conferences

An experiment was conducted to test the efficacy of a new intelligent hypermedia system, MetaTutor, which is intended to prompt and scaffold the use of self-regulated learning (SRL) processes during learning about a human body system. Sixty-eight (N=68) undergraduate students learned about the human circulatory system under one of three conditions: prompt and feedback (PF), prompt-only (PO), and control (C) condition. The PF condition received timely prompts from animated pedagogical agents to engage in planning processes, monitoring processes, and learning strategies and also received immediate directive feedback from the agents concerning the deployment of the processes. The PO condition received the same timely prompts, but did not receive any feedback following the deployment of the processes. Finally, the control condition learned without any assistance from the agents during the learning session. All participants had two hours to learn using a 41-page hypermedia environment which included texts describing and static diagrams depicting various topics concerning the human circulatory system. Results indicate that the PF condition had significantly higher learning efficiency scores, when compared to the control condition. There were no significant differences between the PF and PO conditions. These results are discussed in the context of development of a fully-adaptive hypermedia learning system intended to scaffold self-regulated learning.