Probabilistic Reasoning and Decision Making in Sensory-Motor Systems by Pierre Bessiere, Christian Laugier and Roland Siegwart provides a unique collection of a sizable segment of the cognitive systems research community in Europe. It reports on contributions from leading academic institutions brought together within the European projects Bayesian Inspired Brain and Artifact (BIBA) and Bayesian Approach to Cognitive Systems (BACS). This fourteen-chapter volume covers important research along two main lines: new probabilistic models and algorithms for perception and action, new probabilistic methodology and techniques for artefact conception and development. The work addresses key issues concerned with Bayesian programming, navigation, filte...
In this review we consider how Bayesian logic can help neuroscientists to understand behaviour and b...
When we learn a new motor skill, we have to contend with both the vari-ability inherent in our senso...
How can an incomplete and uncertain model of the environment be used to perceive, infer, decide and ...
Probabilistic Reasoning and Decision Making in Sensory-Motor Systems by Pierre Bessiere, Christian L...
This chapter introduces the probabilistic approach to cognition; describes the different levels of e...
Item does not contain fulltextThis chapter provides an introduction to Bayesian models and their app...
The rational analysis method, first proposed by John R. Anderson, has been enormously influential in...
Cognitive systems, whether human or engineered, must often reason from and act on probabilistic info...
Projet CYBERMOVEThe purpose of this paper is to make a state of the art on probabilistic methodology...
In the domain of modeling sensorimotor systems, whether they are artificial or natural, we are inter...
There has been a recent explosion in research applying Bayesian models to cognitive phenomena. This ...
Book synopsis: The rational analysis method, first proposed by John R. Anderson, has been enormously...
Thesis (Ph.D.)--University of Washington, 2015This dissertation investigates the computational princ...
International audienceHow can an incomplete and uncertain model of the environment be used to percei...
Formal probabilistic models for common cognitive problems. How can an incomplete and uncertain model...
In this review we consider how Bayesian logic can help neuroscientists to understand behaviour and b...
When we learn a new motor skill, we have to contend with both the vari-ability inherent in our senso...
How can an incomplete and uncertain model of the environment be used to perceive, infer, decide and ...
Probabilistic Reasoning and Decision Making in Sensory-Motor Systems by Pierre Bessiere, Christian L...
This chapter introduces the probabilistic approach to cognition; describes the different levels of e...
Item does not contain fulltextThis chapter provides an introduction to Bayesian models and their app...
The rational analysis method, first proposed by John R. Anderson, has been enormously influential in...
Cognitive systems, whether human or engineered, must often reason from and act on probabilistic info...
Projet CYBERMOVEThe purpose of this paper is to make a state of the art on probabilistic methodology...
In the domain of modeling sensorimotor systems, whether they are artificial or natural, we are inter...
There has been a recent explosion in research applying Bayesian models to cognitive phenomena. This ...
Book synopsis: The rational analysis method, first proposed by John R. Anderson, has been enormously...
Thesis (Ph.D.)--University of Washington, 2015This dissertation investigates the computational princ...
International audienceHow can an incomplete and uncertain model of the environment be used to percei...
Formal probabilistic models for common cognitive problems. How can an incomplete and uncertain model...
In this review we consider how Bayesian logic can help neuroscientists to understand behaviour and b...
When we learn a new motor skill, we have to contend with both the vari-ability inherent in our senso...
How can an incomplete and uncertain model of the environment be used to perceive, infer, decide and ...