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» The How and Why of Interactive Markov Chains
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AGENTS
2000
Springer
13 years 9 months ago
Unsupervised clustering of robot activities: a Bayesian approach
Our goal is for robots to learn conceptual systems su cient for natural language and planning. The learning should be autonomous, without supervision. The rst steps in building a ...
Marco Ramoni, Paola Sebastiani, Paul R. Cohen
ATVA
2010
Springer
135views Hardware» more  ATVA 2010»
13 years 6 months ago
Probabilistic Contracts for Component-Based Design
Abstract. We define a probabilistic contract framework for the construction of component-based embedded systems, based on the theory of Interactive Markov Chains. A contract specif...
Dana N. Xu, Gregor Gößler, Alain Giraul...
KDD
2003
ACM
217views Data Mining» more  KDD 2003»
14 years 5 months ago
Algorithms for estimating relative importance in networks
Large and complex graphs representing relationships among sets of entities are an increasingly common focus of interest in data analysis--examples include social networks, Web gra...
Scott White, Padhraic Smyth
WCNC
2010
IEEE
13 years 9 months ago
Throughput Analysis for Cooperation in Random Access Networks
—This paper aims at answering the questions of how to enable cooperative communications in random access networks. And, since cooperation introduces extra transmissions in the ch...
Amr El-Sherif, K. J. Ray Liu
ICTIR
2009
Springer
13 years 3 months ago
An Effective Approach to Verbose Queries Using a Limited Dependencies Language Model
Intuitively, any `bag of words' approach in IR should benefit from taking term dependencies into account. Unfortunately, for years the results of exploiting such dependencies ...
Eduard Hoenkamp, Peter Bruza, Dawei Song, Qiang Hu...