Showing papers by "David Poole published in 2001"

CISpace: tools for learning computational intelligence

Abstract: This paper describes CISpace: tools for learning computational intelligence, a collection of interactive tools for learning AI, and how it fits in with other such tools. CIspace can be found at http://www.cs.ubc.ca/labs/lci/CIspace/.

Symmetric Collaborative Filtering Using the Noisy Sensor Model

Abstract: Collaborative filtering is the process of making recommendations regarding the potential preference of a user, for example shopping on the Internet, based on the preference ratings of the user and a number of other users for various items. This paper considers collaborative filtering based on explicit multivalued ratings. To evaluate the algorithms, we consider only pure collaborative filtering, using ratings exclusively, and no other information about the people or items. Our approach is to predict a user's preferences regarding a particular item by using other people who rated that item and other items rated by the user as noisy sensors. The noisy sensor model uses Bayes' theorem to compute the probability distribution for the user's rating of a new item. We give two variant models: in one, we learn a classical normal linear regression model of how users rate items; in another, we assume different users rate items the same, but the accuracy of the sensors needs to be learned. We compare these variant models with state-of-the-art techniques and show how they are significantly better, whether a user has rated only two items or many. We report empirical results using the EachMovie database of movie ratings. We also show that by considering items similarity along with the users similarity, the accuracy of the prediction increases.

Multi-agent actions under uncertainty: situation calculus, discrete time, plans and policies

Abstract: We are working on a logic to combine the advantages of first-order logic, but using Bayesian decision theory (or more generally game theory) as a basis for handing uncertainty. This forms a logic for multiple agents under uncertainty. These agents act asynchronously, can have their own goals, have noisy sensors, and imperfect effectors. Recently we have developed the independent choice logic that incorporates all of these features. In this paper we discuss two different representations of time within this framework: the situation calculus and what is essentially the event calculus. We show how they both can be used, and compare the different ontological commitments made by each. Uncertainty is handled in terms of a logic which allows for independent choices and a logic program that gives the consequences of the choices. There are probabilities over the choices by nature. As part of the This work was supported by Institute for Robotics and Intelligent Systems, Project IC-7 and Natural Sciences and Engineering Research Council of Canada Operating Grant OGPOO44121.