Results 1  10
of
70
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and ModelBased Approach
 In Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence
, 2000
"... The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. Such systems leverage knowledge about the known preferences of multiple users to recommend items of interest to other users. CF methods have been harnessed to make recommen ..."
Abstract

Cited by 183 (8 self)
 Add to MetaCart
The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. Such systems leverage knowledge about the known preferences of multiple users to recommend items of interest to other users. CF methods have been harnessed to make recommendations about such items as web pages, movies, books, and toys. Researchers have proposed and evaluated many approaches for generating recommendations. We describe and evaluate a new method called personality diagnosis (PD). Given a user's preferences for some items, we compute the probability that he or she is of the same "personality type" as other users, and, in turn, the probability that he or she will like new items. PD retains some of the advantages of traditional similarityweighting techniques in that all data is brought to bear on each prediction and new data can be added easily and incrementally. Additionally, PD has a meaningful probabilistic interpretation, which ma...
Decision Theory in Expert Systems and Artificial Intelligence
 International Journal of Approximate Reasoning
, 1988
"... Despite their different perspectives, artificial intelligence (AI) and the disciplines of decision science have common roots and strive for similar goals. This paper surveys the potential for addressing problems in representation, inference, knowledge engineering, and explanation within the decision ..."
Abstract

Cited by 95 (18 self)
 Add to MetaCart
(Show Context)
Despite their different perspectives, artificial intelligence (AI) and the disciplines of decision science have common roots and strive for similar goals. This paper surveys the potential for addressing problems in representation, inference, knowledge engineering, and explanation within the decisiontheoretic framework. Recent analyses of the restrictions of several traditional AI reasoning techniques, coupled with the development of more tractable and expressive decisiontheoretic representation and inference strategies, have stimulated renewed interest in decision theory and decision analysis. We describe early experience with simple probabilistic schemes for automated reasoning, review the dominant expertsystem paradigm, and survey some recent research at the crossroads of AI and decision science. In particular, we present the belief network and influence diagram representations. Finally, we discuss issues that have not been studied in detail within the expertsystems sett...
Utility Elicitation as a Classification Problem
 IN PROCEEDINGS OF THE FOURTEENTH CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE
, 1998
"... We investigate the application of classification techniques to utility elicitation. In a decision problem, two sets of parameters must generally be elicited: the probabilities and the utilities. While the prior and conditional probabilities in the model do not change from user to user, the util ..."
Abstract

Cited by 53 (2 self)
 Add to MetaCart
We investigate the application of classification techniques to utility elicitation. In a decision problem, two sets of parameters must generally be elicited: the probabilities and the utilities. While the prior and conditional probabilities in the model do not change from user to user, the utility models do. Thus it is necessary to elicit a utility model separately for each new user. Elicitation is
The method of imprecision compared to utility theory for design selection problems
 In Proceedings of the 1993 ASME Design Theory and Methodology Conference
, 1993
"... Two methods have been proposed for manipulating uncertainty reflecting designer choice: utility theory and the method of imprecision. Both methods represent this uncertainty across decision making attributes with zero to one ranks, higher preference modeled with a higher rank. The two methods can di ..."
Abstract

Cited by 24 (5 self)
 Add to MetaCart
(Show Context)
Two methods have been proposed for manipulating uncertainty reflecting designer choice: utility theory and the method of imprecision. Both methods represent this uncertainty across decision making attributes with zero to one ranks, higher preference modeled with a higher rank. The two methods can differ, however, in the combination metrics used to combine the ranks of the incommensurate design attributes. Utility theory resolves the multiattributes using various well proven additive metrics. In contrast, the method of imprecision resolves by also considering nonadditive metrics, such as ranking by the worst case performance or by multiplicative metrics. The axioms of utility theory are appropriate for designs where it is deemed the attributes can always be traded off, even to the point of achieving zero preference in some attributes. In the case of a design with attributes which cannot have zero preference, such as stress limits or maximum allowed cost, the method of imprecision is more appropriate: it trades off attribute levels without permitting any of them to be traded off to zero performance. 1
Mixtures of Gaussians and Minimum Relative Entropy Techniques
 In Uncertainty in Artificial Intelligence: Proceedings of the Ninth Conference: 183–190
, 1993
"... Problems of probabilistic inference and decision making under uncertainty commonly involve continuous random variables. Often these are discretized to a few points, to simplify assessments and computations. An alternative approximation is to fit analytically tractable continuous probability distribu ..."
Abstract

Cited by 24 (2 self)
 Add to MetaCart
(Show Context)
Problems of probabilistic inference and decision making under uncertainty commonly involve continuous random variables. Often these are discretized to a few points, to simplify assessments and computations. An alternative approximation is to fit analytically tractable continuous probability distributions. This approach has potential simplicity and accuracy advantages, especially if variables can be transformed first. This paper shows how a minimum relative entropy criterion can drive both transformation and fitting, illustrating with a power and logarithm family of transformations and mixtures of Gaussian (normal) distributions, which allow use of efficient influence diagram methods. The fitting procedure in this case is the wellknown EM algorithm. The selection of the number of components in a fitted mixture distribution is automated with an objective that trades off accuracy and computational cost. 1
Neuronal substrates for choice under ambiguity, risk, gains, and losses
 Management Science
, 2002
"... Economic forces shape the behavior of individuals and institutions. Forces affecting individual behavior are attitudes about payoffs (gains and losses) and beliefs about outcomes (risk and ambiguity). Under risk, the likelihoods of alternative outcomes are fully known. Under ambiguity, these likelih ..."
Abstract

Cited by 15 (2 self)
 Add to MetaCart
(Show Context)
Economic forces shape the behavior of individuals and institutions. Forces affecting individual behavior are attitudes about payoffs (gains and losses) and beliefs about outcomes (risk and ambiguity). Under risk, the likelihoods of alternative outcomes are fully known. Under ambiguity, these likelihoods are unknown. In our experiment, payoffs and outcomes were manipulated independently during a classical choice task as brain activity was measured with positron emission tomography (PET). Here, we show that attitudes about payoffs and beliefs about the likelihood of outcomes exhibit interaction effects both behaviorally and neurally. Participants are risk averse in gains and riskseeking in losses; they are ambiguityseeking in neither gains nor losses. Two neural substrates for choice surfaced in the interaction between attitudes and beliefs: a dorsomedial neocortical system and a ventromedial system. This finding reveals that the brain does not honor a prevalent assumption of economics—the independence of the evaluations of payoffs and outcomes. The demonstration of a relationship between brain activity and observed economic choice attests to the feasibility of a neuroeconomic decision science.
A Forward Monte Carlo Method for Solving Influence Diagrams Using Local Computation
, 2000
"... The main goal of this paper is to describe a new Monte Carlo method for solving influence diagrams using local computation. We propose a forward Monte Carlo sampling technique that draws independent and identically distributed observations. Methods that have been proposed in this spirit sample from ..."
Abstract

Cited by 10 (2 self)
 Add to MetaCart
The main goal of this paper is to describe a new Monte Carlo method for solving influence diagrams using local computation. We propose a forward Monte Carlo sampling technique that draws independent and identically distributed observations. Methods that have been proposed in this spirit sample from the entire distribution. However, when the number of variables is large, the state space of all variables is exponentially large, and the sample size required for good estimates may be too large to be practical. In this paper, we develop a forward Monte Carlo method, which generates observations from only a small set of chance variables for each decision node in the influence diagram. We use methods developed for exact solution of influence diagrams to limit the number of chance variables sampled at any time. Because influence diagrams model each chance variable with a conditional probability distribution, the forward Monte Carlo solution method lends itself very well to influencediagram representations.
Modeling the User Acceptance of EMail
 in Proceedings of the Thirtysixth Annual Hawaii International Conference on System Sciences (HICSS
, 2003
"... The Technology Acceptance Model (TAM) predicts whether users will ultimately use software applications based upon causal relationships among belief and attitudinal constructs that influence usage behavior. ..."
Abstract

Cited by 9 (2 self)
 Add to MetaCart
(Show Context)
The Technology Acceptance Model (TAM) predicts whether users will ultimately use software applications based upon causal relationships among belief and attitudinal constructs that influence usage behavior.
Towards Flexible MultiAgent DecisionMaking Under Time Pressure
 In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
, 1999
"... To perform rational decisionmaking, autonomous agents need considerable computational resources. In multiagent settings, when other agents are present in the environment, these demands are even more severe. We investigate ways in which the agent's knowledge and the results of deliberative dec ..."
Abstract

Cited by 8 (4 self)
 Add to MetaCart
(Show Context)
To perform rational decisionmaking, autonomous agents need considerable computational resources. In multiagent settings, when other agents are present in the environment, these demands are even more severe. We investigate ways in which the agent's knowledge and the results of deliberative decisionmaking can be compiled to reduce the complexity of decisionmaking procedures and to save time in urgent situations. We use machine learning algorithms to compile decisiontheoretic deliberations into conditionaction rules on how to coordinate in a multiagent environment. Using different learning algorithms, we endow a resourcebounded agent with a tapestry of decision making tools, ranging from purely reactive to fully deliberative ones. The agent can then select a method depending on the time constraints of the particular situation. We also propose combining the decisionmaking tools, so that, for example, more reactive methods serve as a preprocessing stage to the more accurate but sl...
A Bayesian perspective on confidence
 in Uncertainty in Artificial Intelligence
, 1989
"... We present a representation of partial confidence in belief and preference that is consistent with the tenets of decisiontheory. The fundamental insight underlying the representation is that if a person is not completely confident in a probability or utility assessment, additional modeling of the a ..."
Abstract

Cited by 7 (0 self)
 Add to MetaCart
(Show Context)
We present a representation of partial confidence in belief and preference that is consistent with the tenets of decisiontheory. The fundamental insight underlying the representation is that if a person is not completely confident in a probability or utility assessment, additional modeling of the assessment may improve decisions to which it is relevant. We show how a traditional decisionanalytic approach can be used to balance the benefits of additional modeling with associated costs. The approach can be used during knowledge acquisition to focus the attention of a knowledge engineer or expert on parts of a decision model that deserve additional refinement. 1