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174
Making rational decisions using adaptive utility elicitation
- In AAAI
, 2000
"... Rational decision making requires full knowledge of the utility function of the person affected by the decisions. However, in many cases, the task of acquiring such knowledge is not feasible due to the size of the outcome space and the complexity of the utility elicitation process. Given that the am ..."
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Cited by 123 (3 self)
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Rational decision making requires full knowledge of the utility function of the person affected by the decisions. However, in many cases, the task of acquiring such knowledge is not feasible due to the size of the outcome space and the complexity of the utility elicitation process. Given that the amount of utility information we can acquire is limited, we need to make decisions with partial utility information and should carefully select which utility elicitation questions we ask. In this paper, we propose a new approach for this problem that utilizes a prior probability distribution over the person’s utility function, perhaps learned from a population of similar people. The relevance of a utility elicitation question for the current decision problem can then be measured using its value of information. We propose an algorithm that interleaves the analysis of the decision problem and utility elicitation to allow these two tasks to inform each other. At every step, it asks the utility elicitation question giving us the highest value of information and computes the best strategy based on the information acquired so far, stopping when the expected utility loss resulting from our recommendation falls below a pre-specified threshold. We show how the various steps of this algorithm can be implemented efficiently.
Adaptive interfaces and agents
, 2003
"... As its title suggests, this chapter covers a broad range of in-teractive systems. But they all have one idea in common: that it can be worthwhile for a system to learn something about each individual user and adapt its behavior to them in ..."
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Cited by 101 (10 self)
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As its title suggests, this chapter covers a broad range of in-teractive systems. But they all have one idea in common: that it can be worthwhile for a system to learn something about each individual user and adapt its behavior to them in
Background to Qualitative Decision Theory
- AI MAGAZINE
, 1999
"... This paper provides an overview of the field of qualitative decision theory: its motivating tasks and issues, its antecedents, and its prospects. Qualitative decision theory studies qualitative approaches to problems of decision making and their sound and effective reconciliation and integration ..."
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Cited by 95 (4 self)
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This paper provides an overview of the field of qualitative decision theory: its motivating tasks and issues, its antecedents, and its prospects. Qualitative decision theory studies qualitative approaches to problems of decision making and their sound and effective reconciliation and integration with quantitative approaches. Though it inherits from a long tradition, the field offers a new focus on a number of important unanswered questions of common concern to artificial intelligence, economics, law, psychology, and management.
An integrated environment for knowledge acquisition
- IN PROCEEDINGS OF IUI
, 2001
"... This paper describes an integrated acquisition interface that includes several techniques previously developed to support users in various ways as they add new knowledge to an intelligent system. As a result of this integration, the individual techniques can take better advantage of the context in w ..."
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Cited by 80 (12 self)
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This paper describes an integrated acquisition interface that includes several techniques previously developed to support users in various ways as they add new knowledge to an intelligent system. As a result of this integration, the individual techniques can take better advantage of the context in which they are invoked and provide stronger guidance to users. We describe the current implementation using examples from a travel planning domain, and demonstrate how users can add complex knowledge to the system.
A personalized system for conversational recommendations,
- Journal of Artificial Intelligence Research,
, 2004
"... Abstract Increased computing power and the Web have made information widely accessible. In turn, this has encouraged the development of recommendation systems that help users find items of interest, such as books or restaurants. Such systems are more useful when they personalize themselves to each ..."
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Cited by 75 (1 self)
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Abstract Increased computing power and the Web have made information widely accessible. In turn, this has encouraged the development of recommendation systems that help users find items of interest, such as books or restaurants. Such systems are more useful when they personalize themselves to each user's preferences, thus making the recommendation process more efficient and effective. In this paper, we present a new type of recommendation system that carries out a personalized dialogue with the user. This system -the Adaptive Place Advisor -treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. The system incorporates a user model that contains item, attribute, and value preferences, which it updates during each conversation and maintains across sessions. The Place Advisor uses both the conversational context and the user model to retrieve candidate items from a case base. The system then continues to ask questions, using personalized heuristics to select which attribute to ask about next. Then, when only a few items remain, it presents them to the user in a personalized order. We report experimental results demonstrating the effectiveness of user modeling in reducing the time and number of interactions required to find a satisfactory item.
User Modeling in Adaptive Interfaces
- PROCEEDINGS OF THE SEVENTH INTERNATIONAL CONFERENCE ON USER MODELING
, 1999
"... In this paper we examine the notion of adaptive user interfaces, interactive systems that invoke machine learning to improve their interaction with humans. We review some previous work in this emerging area, ranging from software that filters information to systems that support more complex task ..."
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Cited by 75 (6 self)
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In this paper we examine the notion of adaptive user interfaces, interactive systems that invoke machine learning to improve their interaction with humans. We review some previous work in this emerging area, ranging from software that filters information to systems that support more complex tasks like scheduling. After this, we describe three ongoing research efforts that extend this framework in new directions. Finally, we review previous work that has addressed similar issues and consider some challenges that are presented by the design of adaptive user interfaces.
Intrigue: Personalized Recommendation Of Tourist Attractions For Desktop And Handset Devices
- Applied Artificial Intelligence
, 2003
"... This paper presents INTRIGUE, a prototype tourist information server that presents information about the area around Torino city, on desktop and handset devices. This system recommends sightseeing destinations and itineraries by taking into account the preferences of heterogeneous tourist groups ..."
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Cited by 72 (4 self)
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This paper presents INTRIGUE, a prototype tourist information server that presents information about the area around Torino city, on desktop and handset devices. This system recommends sightseeing destinations and itineraries by taking into account the preferences of heterogeneous tourist groups (such as families with children and elderly) and explains the recommendations by addressing the group members' requirements. Moreover the system provides an interactive agenda for scheduling the tour. The services offered by INTRIGUE rely on user modeling and adaptive hypermedia techniques; furthermore, XML-based technologies support the generation of the user interface and its adaptation to Web browsers and WAP minibrowsers.
Using Noninvasive Wearable Computers to Recognize Human Emotions from Physiological Signals
- EURASIP JOURNAL ON APPLIED SIGNAL PROCESSING
, 2004
"... We discuss the strong relationship between affect and cognition and the importance of emotions in multimodal human computer interaction (HCI) and user modeling. We introduce the overall paradigm for our multimodal system that aims at recognizing its users ’ emotions and at responding to them accordi ..."
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Cited by 67 (3 self)
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We discuss the strong relationship between affect and cognition and the importance of emotions in multimodal human computer interaction (HCI) and user modeling. We introduce the overall paradigm for our multimodal system that aims at recognizing its users ’ emotions and at responding to them accordingly depending upon the current context or application. We then describe the design of the emotion elicitation experiment we conducted by collecting, via wearable computers, physiological signals from the autonomic nervous system (galvanic skin response, heart rate, temperature) and mapping them to certain emotions (sadness, anger, fear, surprise, frustration, and amusement). We show the results of three different supervised learning algorithms that categorize these collected signals in terms of emotions, and generalize their learning to recognize emotions from new collections of signals. We finally discuss possible broader impact and potential applications of emotion recognition for multimodal intelligent systems.
On graphical modeling of preference and importance
, 2006
"... In recent years, CP-nets have emerged as a useful tool for supporting preference elicitation, reasoning, and representation. CP-nets capture and support reasoning with qualitative conditional preference statements, statements that are relatively natural for users to express. In this paper, we extend ..."
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Cited by 63 (6 self)
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In recent years, CP-nets have emerged as a useful tool for supporting preference elicitation, reasoning, and representation. CP-nets capture and support reasoning with qualitative conditional preference statements, statements that are relatively natural for users to express. In this paper, we extend the CP-nets formalism to handle another class of very natural qualitative statements one often uses in expressing preferences in daily life – statements of relative importance of attributes. The resulting formalism, TCP-nets, maintains the spirit of CP-nets, in that it remains focused on using only simple and natural preference statements, uses the ceteris paribus semantics, and utilizes a graphical representation of this information to reason about its consistency and to perform, possibly constrained, optimization using it. The extra expressiveness it provides allows us to better model tradeoffs users would like to make, more faithfully representing their preferences. 1.
Survey of Preference Elicitation Methods
- Ecole Politechnique Federale de Lausanne (EPFL), IC/2004/67
, 2004
"... As people increasingly rely on interactive decision support systems to choose products and make decisions, building effective interfaces for these systems becomes more and more challenging due to the explosion of on-line information, the initial incomplete user preference and user’s cognitive and em ..."
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Cited by 60 (1 self)
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As people increasingly rely on interactive decision support systems to choose products and make decisions, building effective interfaces for these systems becomes more and more challenging due to the explosion of on-line information, the initial incomplete user preference and user’s cognitive and emotional limitations of information processing. How to accurately elicit user’s preference thereby becomes the main concern of current decision support systems. This paper is a survey of the typical preference elicitation methods proposed by related research works, starting from the traditional utility function elicitation and analytic hierarchy process methods, to computer aided elicitation approaches which include example critiquing, needs-oriented interaction, comparison matrix, CP-network, preferences clustering & matching and collaborative filtering.
