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User Modeling and User-Adapted Interaction 8

Editors:Alfred Kobsa
Dates:1998
Volume:8
Publisher:Springer
Standard No:ISSN 0924-1868 (print) EISSN 1573-1391 (online)
Papers:13
Links:link.springer.com | Table of Contents
  1. UMUAI 1998 Volume 8 Issue 1/2
  2. UMUAI 1998 Volume 8 Issue 3/4

UMUAI 1998 Volume 8 Issue 1/2

Special Issue on Machine Learning for User Modeling

Preface to UMUAI Special Issue on Machine Learning for User Modeling BIBFull-Text 1-3
  G. Webb
Bayesian Models for Keyhole Plan Recognition in an Adventure Game BIBAKFull-Text 5-47
  David W. Albrecht; Ingrid Zukerman
We present an approach to keyhole plan recognition which uses a dynamic belief (Bayesian) network to represent features of the domain that are needed to identify users' plans and goals. The application domain is a Multi-User Dungeon adventure game with thousands of possible actions and locations. We propose several network structures which represent the relations in the domain to varying extents, and compare their predictive power for predicting a user's current goal, next action and next location. The conditional probability distributions for each network are learned during a training phase, which dynamically builds these probabilities from observations of user behaviour. This approach allows the use of incomplete, sparse and noisy data during both training and testing. We then apply simple abstraction and learning techniques in order to speed up the performance of the most promising dynamic belief networks without a significant change in the accuracy of goal predictions. Our experimental results in the application domain show a high degree of predictive accuracy. This indicates that dynamic belief networks in general show promise for predicting a variety of behaviours in domains which have similar features to those of our domain, while reduced models, obtained by means of learning and abstraction, show promise for efficient goal prediction in such domains.
Keywords: Plan recognition; Bayesian Belief Networks; language learning; abstraction; performance evaluation
Bayesian Update of Recursive Agent Models BIBAKFull-Text 49-69
  Piotr J. Gmytrasiewicz; Sanguk Noh
We present a framework for Bayesian updating of beliefs about models of agent(s) based on their observed behavior. We work within the formalism of the Recursive Modeling Method (RMM) that maintains and processes models an agent may use to interact with other agent(s), the models the agent may think the other agent has of the original agent, the models the other agent may think the agent has, and so on. The beliefs about which model is the correct one are incrementally updated based on the observed behavior of the modeled agent and, as the result, the probability of the model that best predicted the observed behavior is increased. Analogously, the models on deeper levels of modeling can be updated; the models that the agent thinks another agent uses to model the original agent are revised based on how the other agent is expected to observe the original agent's behavior, and so on. We have implemented and tested our method in two domains, and the results show a marked improvement in the quality of interactions with the belief update in both domains.
Keywords: Bayesian learning; probabilistic updating; agent models; coordination; air defense; decision theory; multi-agent; artificial intelligence
Exploring Versus Exploiting when Learning User Models for Text Recommendation BIBAKFull-Text 71-102
  Marko Balabanovic
The text recommendation task involves delivering sets of documents to users on the basis of user models. These models are improved over time, given feedback on the delivered documents. When selecting documents to recommend, a system faces an instance of the exploration/exploitation tradeoff: whether to deliver documents about which there is little certainty, or those which are known to match the user model learned so far. In this paper, a simulation is constructed to investigate the effects of this tradeoff on the rate of learning user models, and the resulting compositions of the sets of recommended documents, in particular World-Wide Web pages. Document selection strategies are developed which correspond to different points along the tradeoff. Using an exploitative strategy, our results show that simple preference functions can successfully be learned using a vector-space representation of a user model in conjunction with a gradient descent algorithm, but that increasingly complex preference functions lead to a slowing down of the learning process. Exploratory strategies are shown to increase the rate of user model acquisition at the expense of presenting users with suboptimal recommendations; in addition they adapt to user preference changes more rapidly than exploitative strategies. These simulated tests suggest an implementation for a simple control that is exposed to users, allowing them to vary a system's document selection behavior depending on individual circumstances.
Keywords: Recommender systems; Information filtering; User modeling; Relevance feedback; Selective Dissemination of Information; Machine learning; adaptive information retrieval
Discovering Error Classes from Discrepancies in Novice Behaviors Via Multistrategy Conceptual Clustering BIBAKFull-Text 103-129
  Raymund Sison; Masayuki Numao
The automatic discovery of classes of errors that represent misconceptions and other knowledge errors underlying discrepancies in novice behavior is not a trivial task. A novel approach to this problem is described, in which relationships among behavioral discrepancies are analyzed and inductively generalized via an unsupervised, incremental, relational multistrategy conceptual clustering method that takes into account similarities as well as causalities in the data. Performance results on the classification of discrepancy sets and discovery of error classes from discrepancies of buggy PROLOG programs demonstrate the potential of the approach.
Keywords: student modeling; multistrategy learning; unsupervised learning; conceptual clustering
Using Decision Trees for Agent Modeling: Improving Prediction Performance BIBAKFull-Text 131-152
  Bark Cheung Chiu; Geoffrey I. Webb
A modeling system may be required to predict an agent's future actions under constraints of inadequate or contradictory relevant historical evidence. This can result in low prediction accuracy, or otherwise, low prediction rates, leaving a set of cases for which no predictions are made. A previous study that explored techniques for improving prediction rates in the context of modeling students' subtraction skills using Feature Based Modeling showed a tradeoff between prediction rate and predication accuracy. This paper presents research that aims to improve prediction rates without affecting prediction accuracy. The FBM-C4.5 agent modeling system was used in this research. However, the techniques explored are applicable to any Feature Based Modeling system, and the most effective technique developed is applicable to most agent modeling systems. The default FBM-C4.5 system models agents' competencies with a set of decision trees, trained on all historical data. Each tree predicts one particular aspect of the agent's action. Predictions from multiple trees are compared for consensus. FBM-C4.5 makes no prediction when predictions from different trees contradict one another. This strategy trades off reduced prediction rates for increased accuracy. To make predictions in the absence of consensus, three techniques have been evaluated. They include using voting, using a tree quality measure and using a leaf quality measure. An alternative technique that merges multiple decision trees into a single tree provides an advantage of producing models that are more comprehensible. However, all of these techniques demonstrated the previous encountered trade-off between rate of prediction and accuracy of prediction, albeit less pronounced. It was hypothesized that models built on more current observations would outperform models built on earlier observations. Experimental results support this hypothesis. A Dual-model system, which takes this temporal factor into account, has been evaluated. This fifth approach achieved a significant improvement in prediction rate without significantly affecting prediction accuracy.
Keywords: Agent modeling; Student modeling; Inductive learning; Decision tree

Book Review

Context and Consciousness: Activity Theory and Human Computer Interaction, Bonnie A. Nardi (ed.) BIBFull-Text 153-157
  Antonio Rizzo; Marco Palmonari
Case Based Reasoning, by Janet Kolodner BIBFull-Text 157-160
  M. Sasikumar

UMUAI 1998 Volume 8 Issue 3/4

Preface BIBFull-Text 167-170
  Susan Haller; Susan McRoy
What is Initiative? BIBAKFull-Text 171-214
  Robin Cohen; Coralee Allaby; Christian Cumbaa
This paper presents some alternate theories for explaining the term 'initiative', as it is used in the design of mixed-initiative AI systems. Although there is now active research in the area of mixed initiative interactive systems, there appears to be no true consensus in the field as to what the term 'initiative' actually means. In describing different possible approaches to the modeling of initiative, we aim to show the potential importance of each particular theory for the design of mixed initiative systems. The paper concludes by summarizing some of the key points in common to the theories, and by commenting on the inherent difficulties of the exercise, thereby elucidating the limitations which are necessarily encountered in designing such theories as the basis for designing mixed-initiative systems.
Keywords: Initiative; discourse; goals and plans
An Evidential Model for Tracking Initiative in Collaborative Dialogue Interactions BIBAKFull-Text 215-254
  Jennifer Chu-Carroll; Michael K. Brown
In this paper, we argue for the need to distinguish between task initiative and dialogue initiative, and present an evidential model for tracking shifts in both types of initiatives in collaborative dialogue interactions. Our model predicts the task and dialogue initiative holders for the next dialogue turn based on the current initiative holders and the effect that observed cues have on changing them. Our evaluation across various corpora shows that the use of cues consistently provides significant improvement in the system's prediction of task and dialogue initiative holders. Finally, we show how this initiative tracking model may be employed by a dialogue system to enable the system to tailor its responses to user utterances based on application domain, system's role in the domain, dialogue history, and user characteristics.
Keywords: Initiative; control; dialogue systems; collaborative interactions
An Analysis of Initiative Selection in Collaborative Task-Oriented Discourse BIBAKFull-Text 255-314
  Curry I. Guinn
In this paper we propose a number of principles and conjectures for mixed-initiative collaborative dialogs. We explore some methodologies for managing initiative between conversational participants. We mathematically analyze specific initiative-changing mechanisms based on a probabilistic knowledge base and user model. We look at the role of negotiation in managing initiative and quantify how the negotiation process is useful toward modifying user models. Some experimental results using computer-computer simulations are presented along with some discussion of how such studies are useful toward building human-computer systems.
Keywords: Dialog; mixed-initiative; collaboration; dialog initiative; task initiative; negotiation; computer-computer dialogs
COLLAGEN: A Collaboration Manager for Software Interface Agents BIBAKFull-Text 315-350
  Charles Rich; Candace L. Sidner
We have implemented an application-independent collaboration manager, called Collagen, based on the SharedPlan theory of discourse, and used it to build a software interface agent for a simple air travel application. The software agent provides intelligent, mixed initiative assistance without requiring natural language understanding. A key benefit of the collaboration manager is the automatic construction of an interaction history which is hierarchically structured according to the user's and agent's goals and intentions.
Keywords: Agent; collaboration; mixed initiative; SharedPlan; discourse; segment; interaction history