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Monitoring systems have become increasingly prevalent in order to increase the safety of elderly people who live alone. These systems are designed to raise alerts when adverse events are detected, which in turn enables family and carers to take action in a timely manner. However, monitoring systems typically suffer from two problems: they may generate false alerts or miss true adverse events.This motivates the two user studies presented in this paper: (1) in the first study, we investigate the effect of the performance of different monitoring systems, in terms of accuracy and error type, on users’ trust in these systems and behaviour; and (2) in the second study, we examine the effect of recommendations made by an advisor agent on users’ behaviour.Our user studies take the form of a web-based game set in a retirement village, where elderly residents live in smart homes equipped with monitoring systems. Players, who “work” in the village, perform a primary task whereby they must ensure the welfare of the residents by attending to adverse events in a timely manner, and a secondary routine task that demands their attention. These conditions are typical of a retirement setting, where workers perform various duties in addition to keeping an eye on a monitoring system.Our main findings pertain to: (1) the identification of user types that shed light on users’ trust in automation and aspects of their behaviour; (2) the effect of monitoring-system accuracy and error type on users’ trust and behaviour; (3) the effect of the recommendations made by an advisor agent on users’ behaviour; and (4) the identification of influential factors in models that predict users’ trust and behaviour. The studies that yield these findings are enabled by two methodological contributions: (5) the game itself, which supports experimentation with various factors, and a version of the game augmented with an advisor agent; and (6) techniques for calibrating the parameters of the game and determining the recommendations of the advisor agent.

This paper presents results from a web-based study that investigates users’ attitudes toward smart devices, focusing on acceptability. Specifically, we conducted a survey that elicits users’ ratings of devices in isolation and devices in the context of tasks potentially performed by these devices. Our study led to insights about users’ attitudes towards devices in isolation and in the context of tasks, and about the influence of demographic factors and factors pertaining to technical expertise and experience with devices on users’ attitudes. The insights about users’ attitudes provided the basis for two recommendation approaches based on principal components analysis (PCA) that alleviate the new-user and new-item problems: (1) employing latent features identified by PCA to predict ratings given by existing users to new devices, and by new users to existing devices; and (2) identifying a relatively small set of key questions on the basis of PCs, whose answers account to a large extent for new users’ ratings of devices in isolation and in the context of tasks. Our results show that taking into account latent features of devices, and asking a relatively small number of key questions about devices in the context of tasks, lead to rating predictions that are significantly more accurate than global and demographic predictions, and substantially reduce prediction error, eventually matching the performance of strong baselines.

Despite recent advances in automatic speech recognition, one of the main stumbling blocks to the widespread adoption of Spoken Dialogue Systems is the lack of reliability of automatic speech recognizers. In this paper, we offer a two-tier error-correction process that harnesses syntactic, semantic and pragmatic information to improve the understanding of spoken referring expressions, specifically descriptions of objects in physical spaces. A syntactic-semantic tier offers generic corrections to perceived ASR errors on the basis of syntactic expectations of a semantic model, and passes the corrected texts to a language understanding system. The output of this system, which consists of pragmatic interpretations, is then refined by a contextual-phonetic tier, which prefers interpretations that are phonetically similar to the mis-heard words. Our results, obtained on a corpus of 341 referring expressions, show that syntactic-semantic error correction significantly improves interpretation performance, and contextual-phonetic refinements yield further improvements.

HighlightsAn interpretation process that considers multiple alternatives.A mechanism for combining uncertainty from a variety of sources.Distance functions with probabilistic semantics that represent similarity measures.Two evaluation experiments to assess our system's performance. In this paper, we present Scusi?, an anytime numerical mechanism for the interpretation of spoken referring expressions. Our contributions are: (1) an anytime interpretation process that considers multiple alternatives at different interpretation stages (speech, syntax, semantics and pragmatics), which enables Scusi? to defer decisions to the end of the interpretation process; (2) a mechanism that combines scores associated with the output of the different interpretation stages, taking into account the uncertainty arising from a variety of sources, such as ambiguity or inaccuracy in a description, speech recognition errors and out-of-vocabulary terms; and (3) distance-based functions with probabilistic semantics that represent lexical similarity between objects' names and similarity between stated requirements and physical properties of objects (viz colour, size and positional relations). We considered two approaches for combining these descriptive attributes, viz multiplicative and additive, and determined whether prioritizing certain interpretation stages and descriptive attributes affects interpretation performance. We conducted two experiments to evaluate different aspects of Scusi?'s performance: Interpretive, where we compared Scusi?'s understanding of descriptions that are mainly ambiguous or inaccurate with people's understanding of these descriptions, and Generative, where we assessed Scusi?'s understanding of naturally occurring spoken descriptions. Our results show that Scusi?'s understanding of the descriptions in the Interpretive trial is comparable to that of people; and that its performance is encouraging when given arbitrary spoken descriptions in diverse scenarios, and excellent for the corresponding written descriptions. In both experiments, Scusi? significantly outperformed a baseline system that maintains only top same-score interpretations.

People are often overwhelmed by the large amount of information provided in museum spaces, which makes it difficult for them to select exhibits of potential interest. As a first step in recommending exhibits where a visitor may wish to spend some time, this article investigates predictive user models for personalised prediction of museum visitors' viewing times at exhibits. We consider two content-based models and a nearest-neighbour collaborative filter, and develop a collaborative model based on the theory of spatial processes which relies on a notion of distance between exhibits. We discuss models of exhibit distance derived from viewing-time similarity, semantic similarity and walking distance. The results from our evaluation with a real-world dataset of visitor pathways collected at Melbourne Museum (Melbourne, Australia) suggest that utilising walking and semantic distances between exhibits enables more accurate predictions of a visitor's viewing times of unseen exhibits than using distances derived from observed exhibit viewing times. Our results also show that all models outperform a non-personalised baseline, that content-based viewing time prediction yields better results than nearest-neighbour collaborative prediction, and that our collaborative model based on spatial processes attains the highest predictive accuracy overall.

While identifying the intention of an utterance has played a major role in natural language understanding, this work is the first to extend intention recognition to the domain of information graphics. A tenet of this work is the belief that information graphics are a form of language. This is supported by the observation that the overwhelming majority of information graphics from popular media sources appear to have some underlying goal or intended message. As Clark noted, language is more than just words. It is any “signal” (or lack of signal when one is expected), where a signal is a deliberate action that is intended to convey a message (Clark, 1996 [15]).As a form of language, information graphics contain communicative signals that can be used in a computational system to identify the message that the graphic conveys. We identify the communicative signals that appear in simple bar charts, and present an implemented Bayesian network methodology for reasoning about these signals and hypothesizing a bar chart's intended message. Once the message conveyed by an information graphic has been inferred, it can then be used to facilitate access to this information resource for a variety of users, including 1) users of digital libraries, 2) visually impaired users, and 3) users of devices where graphics are impractical or inaccessible.

Statistical and probabilistic models are concerned with the use of observed sample results to make statements about unknown, dependent parameters. In user modeling, these parameters represent aspects of a user’s behaviour, such as his or her goals, preferences, and forthcoming actions or locations. Recent technological advances, in particular increased computational power, together with anytime, anyplace access to computers, and the information explosion associatedwith the Internet, provide new opportunities for information dissemination and information gathering. On one hand, people have access to large repositories of information in digital form. On the other hand, information providers can find out more about their users’ requirements by logging people’s activities. This mixture of vast electronic content and increased knowledge about people’s actions provides an opportunity to harness statistical and probabilistic models to build user models that support the delivery of personalized content. This usage of statistical and probabilistic models has been manifested in UMUAI for the last ten years. Particularly noteworthy are the articles in the Special Issue on Machine Learning for User Modeling (1998); the survey articles by Zukerman and Albrecht and byWebb et al. in the 10-year anniversary issue, respectively on predictive statistical models for user modeling (Zukerman and Albrecht 2001), and on machine learning for user modeling (Webb et al. 2001); Burke’s survey on recommender systems (Burke 2002); and Pierrakos et al.’s survey on Web usage mining (Pierrakos et al. 2003). These articles identified several challenges that user modeling presents to statistical and probabilistic modeling techniques. We classify these challenges into three categories: (1) limitations of current user modeling approaches, (2) dynamic nature of user modeling data, and (3) efficiency considerations.

We describe a probabilistic approach for the interpretation of user arguments, and investigate the incorporation of different models of a user's beliefs and inferences into this mechanism. Our approach is based on the tenet that the interpretation intended by the user is that with the highest posterior probability. This approach is implemented in a computer-based detective game, where the user explores a virtual scenario, and constructs an argument for a suspect's guilt or innocence. Our system receives as input an argument entered through a web interface, and produces an interpretation in terms of its underlying knowledge representation -- a Bayesian network. This interpretation may differ from the user's argument in its structure and in its beliefs in the argument propositions. We conducted a synthetic evaluation of the basic interpretation mechanism, and a user-based evaluation which assesses the impact of the different user models. The results of both evaluations were encouraging, with the system generally producing argument interpretations our users found acceptable.

The fields of user modeling and natural language processing have been closely linked since the early days of user modeling. Natural language systems consult user models in order to improve their understanding of users' requirements and to generate appropriate and relevant responses. At the same time, the information natural language systems obtain from their users is expected to increase the accuracy of their user models. In this paper, we review natural language systems for generation, understanding and dialogue, focusing on the requirements and limitations these systems and user models place on each other. We then propose avenues for future research.

The limitations of traditional knowledge representation methods for modeling complex human behaviour led to the investigation of statistical models. Predictive statistical models enable the anticipation of certain aspects of human behaviour, such as goals, actions and preferences. In this paper, we motivate the development of these models in the context of the user modeling enterprise. We then review the two main approaches to predictive statistical modeling, content-based and collaborative, and discuss the main techniques used to develop predictive statistical models. We also consider the evaluation requirements of these models in the user modeling context, and propose topics for future research.

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.

Most Natural Language Generation systems developed to date assume that a user will learn only what is explicitly stated in the discourse. This assumption leads to the generation of discourse that states explicitly all the information to be conveyed, and does not address further inferences from the discourse. In this paper, we describe a student model which provides a qualitative representation of a student's beliefs and inferences, and a content planning mechanism which consults this model in order to address the above problems. Our mechanism performs inferences in backward reasoning mode to generate discourse that conveys the intended information, and in forward reasoning mode to draw conclusions from the presented information. The forward inferences enable our mechanism to address possible incorrect inferences from the discourse, and to omit information that may be easily inferred from the discourse. In addition, our mechanism improves the conciseness of the generated discourse by omitting information known by the student. The domain of our implementation is the explanation of concepts in high school algebra.

This paper presents a mechanism which infers a user's plans from his/her utterances by directing the inference process towards the more likely interpretations of a speaker's statements among many possible interpretations. Our mechanism uses Bayesian theory of probability to assess the likelihood of an interpretation, and it complements this assessment by taking into consideration two aspects of an interpretation: its coherence and its information content. The coherence of an interpretation is determined by the relationships between the different statements in the discourse. The information content of an interpretation is a measure of how well defined the interpretation is in terms of the actions to be performed on the basis of this interpretation. This measure is used to guide the inference process towards interpretations with higher information content. The information content of an interpretation depends on the specificity and the certainty of the inferences in it, where the certainty of an inference depends on the knowledge on which the inference is based. Our mechanism has been developed for use in task-oriented consultation systems. The particular domain that we have chosen for exploration is that of travel booking.

Human discourse is fraught with rhetorical devices such as contradictions, illustrations, and analogies. These rhetorical devices carry important information which a listener uses to speed up the comprehension process. In this paper, we present a taxonomy of rhetorical devices commonly used in tutoring environment, and model the meaning of a class of rhetorical devices in terms of their anticipated effect on a listener's knowledge. These predictions are then used in planning computer-generated discourse. As a testbed for our ideas, a system called WISHFUL is being implemented to generate commentaries in the domain of high-school algebra within the framework of an intelligent tutoring system.

In this paper, we present a unified approach to handling uncertainty during plan inference in cooperative consultations. This approach assists with the following aspects of the plan inference process: inferring a user's intentions among a number of possibilities, deciding whether to admit an unlikely interpretation of the user's request or to actively acquire information from the user, determining whether a perceived ambiguity in a user's request is to be resolved by heuristics or by soliciting information from the user, and deciding whether a recognized intention is sufficiently detailed so that a plan may be proposed to satisfy it. We define an information-theoretic measure which allows us to determine the amount of information in an interpretation of a user's request, and show how this measure is combined with probabilities of interpretations to give preference to interpretations that are better defined. Our approach is implemented as part of a computerized consultant that operates as a travel agent.

We explore the problem of how to perform complex strategic planning in an uncertain domain. Although card games such as bridge and poker provide interesting domains for exploring uncertainty, they tend to be strategically simple given perfect information. We present and discuss a strategically complex, easily controlled, imperfect information domain-a variant of chess-which allows us to explore this difficult problem. We also describe a modular hybrid architecture that uses game theory, information theory and utility theory to play this new game. We discuss preliminary results from our architecture and consider possible future directions for the project.

We describe a probabilistic approach for the interpretation of user arguments that integrates three aspects of an interpretation: inferences, suppositions and explanatory extensions. Inferences fill in information that connects the propositions in a user's argument, suppositions postulate new information that is likely believed by the user and is necessary to make sense of his or her argument, and explanatory extensions postulate information the user may have implicitly considered when constructing his or her argument. Our system receives as input an argument entered through a web interface, and produces an interpretation in terms of its underlying knowledge representation--a Bayesian network. Our evaluations show that suppositions and explanatory extensions are necessary components of interpretations, and that users consider appropriate the suppositions and explanatory extensions postulated by our system.