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22
Statistical relational learning for link prediction
 In Proceedings of the Workshop on Learning Statistical Models from Relational Data at IJCAI2003
, 2003
"... Link prediction is a complex, inherently relational, task. Be it in the domain of scientific citations, social networks or hypertext links, the underlying data are extremely noisy and the characteristics useful for prediction are not readily available in a “flat ” file format, but rather involve com ..."
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Cited by 87 (6 self)
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Link prediction is a complex, inherently relational, task. Be it in the domain of scientific citations, social networks or hypertext links, the underlying data are extremely noisy and the characteristics useful for prediction are not readily available in a “flat ” file format, but rather involve complex relationships among objects. In this paper, we propose the application of our methodology for Statistical Relational Learning to building link prediction models. We propose an integrated approach to building regression models from data stored in relational databases in which potential predictors are generated by structured search of the space of queries to the database, and then tested for inclusion in a logistic regression. We present experimental results for the task of predicting citations made in scientific literature using relational data taken from CiteSeer. This data includes the citation graph, authorship and publication venues of papers, as well as their word content. 1
Learning Statistical Models from Relational Data
, 2001
"... This workshop is the second in a series of workshops held in conjunction with AAAI and IJCAI. The first workshop was held in July, 2000 at AAAI. Notes from that workshop are available at ..."
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Cited by 48 (6 self)
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This workshop is the second in a series of workshops held in conjunction with AAAI and IJCAI. The first workshop was held in July, 2000 at AAAI. Notes from that workshop are available at
Structural Logistic Regression for Link Analysis
, 2003
"... We present Structural Logistic Regression, an extension of logistic regression to modeling relational data. It is an integrated approach to building regression models from data stored in relational databases in which potential predictors, both boolean and realvalued, are generated by structured ..."
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Cited by 33 (6 self)
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We present Structural Logistic Regression, an extension of logistic regression to modeling relational data. It is an integrated approach to building regression models from data stored in relational databases in which potential predictors, both boolean and realvalued, are generated by structured search in the space of queries to the database, and then tested with statistical information criteria for inclusion in a logistic regression. Using statistics and relational representation allows modeling in noisy domains with complex structure. Link prediction is a task of high interest with exactly such characteristics. Be it in the domain of scientific citations, social networks or hypertext, the underlying data are extremely noisy and the features useful for prediction are not readily available in a "flat" file format. We propose the application of Structural Logistic Regression to building link prediction models, and present experimental results for the task of predicting citations made in scientific literature using relational data taken from the CiteSeer search engine. This data includes the citation graph, authorship and publication venues of papers, as well as their word content.
A MonteCarlo AIXI Approximation
, 2009
"... This paper describes a computationally feasible approximation to the AIXI agent, a universal reinforcement learning agent for arbitrary environments. AIXI is scaled down in two key ways: First, the class of environment models is restricted to all prediction suffix trees of a fixed maximum depth. Thi ..."
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Cited by 28 (9 self)
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This paper describes a computationally feasible approximation to the AIXI agent, a universal reinforcement learning agent for arbitrary environments. AIXI is scaled down in two key ways: First, the class of environment models is restricted to all prediction suffix trees of a fixed maximum depth. This allows a Bayesian mixture of environment models to be computed in time proportional to the logarithm of the size of the model class. Secondly, the finitehorizon expectimax search is approximated by an asymptotically convergent Monte Carlo Tree Search technique. This scaled down AIXI agent is empirically shown to be effective on a wide class of toy problem domains, ranging from simple fully observable games to small POMDPs. We explore the limits of this approximate agent and propose a general heuristic framework for scaling this technique to much larger problems.
A Monte Carlo AIXI Approximation
 J. Artif. Intell. Res
"... This paper describes a computationally feasible approximation to the AIXI agent, a universal reinforcement learning agent for arbitrary environments. AIXI is scaled down in two key ways: First, the class of environment models is restricted to all prediction suffix trees of a fixed maximum depth. Thi ..."
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Cited by 21 (11 self)
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This paper describes a computationally feasible approximation to the AIXI agent, a universal reinforcement learning agent for arbitrary environments. AIXI is scaled down in two key ways: First, the class of environment models is restricted to all prediction suffix trees of a fixed maximum depth. This allows a Bayesian mixture of environment models to be computed in time proportional to the logarithm of the size of the model class. Secondly, the finitehorizon expectimax search is approximated by an asymptotically convergent Monte Carlo Tree Search technique. This scaled down AIXI agent is empirically shown to be effective on a wide class of toy problem domains, ranging from simple fully observable games to small POMDPs. We explore the limits of this approximate agent and propose a general heuristic framework for scaling this technique to much larger problems.
Learning minesweeper with multirelational learning
 In Proc. of the 18th IJCAI
, 2003
"... Minesweeper is a oneperson game which looks deceptively easy to play, but where average human performance is far from optimal. Playing the game requires logical, arithmetic and probabilistic reasoning based on spatial relationships on the board. Simply checking a board state for consistency is an N ..."
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Cited by 8 (1 self)
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Minesweeper is a oneperson game which looks deceptively easy to play, but where average human performance is far from optimal. Playing the game requires logical, arithmetic and probabilistic reasoning based on spatial relationships on the board. Simply checking a board state for consistency is an NPcomplete problem. Given the difficulty of handcrafting strategies to play this and other games, AI researchers have always been interested in automatically learning such strategies from experience. In this paper, we show that when integrating certain techniques into a general purpose learning system (Mio), the resulting system is capable of inducing a Minesweeper playing strategy that beats the winning rate of average human players. In addition, we discuss the necessary background knowledge, present experimental results demonstrating the gain obtained with our techniques and show the strategy learned for the game. 1
A Comparative Study on Methods for Reducing Myopia of HillClimbing Search in Multirelational Learning
 In Proc. of the 21st ICML
, 2004
"... Hillclimbing search is the most commonly used search algorithm in ILP systems because it permits the generation of theories in short running times. However, a well known drawback of this greedy search strategy is its myopia. ..."
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Cited by 7 (1 self)
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Hillclimbing search is the most commonly used search algorithm in ILP systems because it permits the generation of theories in short running times. However, a well known drawback of this greedy search strategy is its myopia.
Logicbased information integration and machine learning for gene regulation prediction
 In Proceedings of the Ninth International Conference on Molecular Systems Biology
, 2006
"... One of the central goals in computational and systems biology is to understand the mechanisms of gene transcriptional regulation on a systemwide level. The efforts are often based on highthroughput genomic data of model organisms ..."
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Cited by 3 (0 self)
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One of the central goals in computational and systems biology is to understand the mechanisms of gene transcriptional regulation on a systemwide level. The efforts are often based on highthroughput genomic data of model organisms
Predicate selection for structural decision trees
 Proceedings of the 15th International Conference on Inductive Logic Programming, LNAI3625
, 2005
"... Abstract. We study predicate selection functions (also known as splitting rules) for structural decision trees and propose two improvements to existing schemes. The first is in classification learning, where we reconsider the use of accuracy as a predicate selection function and show that, on practi ..."
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Cited by 2 (1 self)
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Abstract. We study predicate selection functions (also known as splitting rules) for structural decision trees and propose two improvements to existing schemes. The first is in classification learning, where we reconsider the use of accuracy as a predicate selection function and show that, on practical grounds, it is a better alternative to other commonly used functions. The second is in regression learning, where we consider the standard mean squared error measure and give a predicate pruning result for it. 1
Inverse Reinforcement Learning in Relational Domains
"... In this work, we introduce the first approach to the Inverse Reinforcement Learning (IRL) problem in relational domains. IRL has been used to recover a more compact representation of the expert policy leading to better generalization performances among different contexts. On the other hand, relati ..."
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Cited by 1 (1 self)
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In this work, we introduce the first approach to the Inverse Reinforcement Learning (IRL) problem in relational domains. IRL has been used to recover a more compact representation of the expert policy leading to better generalization performances among different contexts. On the other hand, relational learning allows representing problems with a varying number of objects (potentially infinite), thus provides more generalizable representations of problems and skills. We show how these different formalisms allow one to create a new IRL algorithm for relational domains that can recover with great efficiency rewards from expert data that have strong generalization and transfer properties. We evaluate our algorithm in representative tasks and study the impact of diverse experimental conditions such as: the number of demonstrations, knowledge about the dynamics, transfer among varying dimensions of a problem, and changing dynamics. 1