Results 1  10
of
22
Learning object affordances: From sensory–motor coordination to imitation
 IEEE TRANSACTIONS ON ROBOTICS
, 2008
"... Affordances encode relationships between actions, objects, and effects. They play an important role on basic cognitive capabilities such as prediction and planning. We address the problem of learning affordances through the interaction of a robot with the environment, a key step to understand the w ..."
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Cited by 31 (6 self)
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Affordances encode relationships between actions, objects, and effects. They play an important role on basic cognitive capabilities such as prediction and planning. We address the problem of learning affordances through the interaction of a robot with the environment, a key step to understand the world properties and develop social skills. We present a general model for learning object affordances using Bayesian networks integrated within a general developmental architecture for social robots. Since learning is based on a probabilistic model, the approach is able to deal with uncertainty, redundancy, and irrelevant information. We demonstrate successful learning in the real world by having an humanoid robot interacting with objects. We illustrate the benefits of the acquired knowledge in imitation games.
Bayesian structure learning using dynamic programming and MCMC
 In UAI, 2007b
"... We show how to significantly speed up MCMC sampling of DAG structures by using a powerful nonlocal proposal based on Koivisto’s dynamic programming (DP) algorithm (11; 10), which computes the exact marginal posterior edge probabilities by analytically summing over orders. Furthermore, we show how s ..."
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Cited by 13 (1 self)
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We show how to significantly speed up MCMC sampling of DAG structures by using a powerful nonlocal proposal based on Koivisto’s dynamic programming (DP) algorithm (11; 10), which computes the exact marginal posterior edge probabilities by analytically summing over orders. Furthermore, we show how sampling in DAG space can avoid subtle biases that are introduced by approaches that work only with orders, such as Koivisto’s DP algorithm and MCMC order samplers (6; 5). 1
Exact structure discovery in Bayesian networks with less space
 In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI
, 2009
"... The fastest known exact algorithms for scorebased structure discovery in Bayesian networks on n nodes run in time and space 2 n n O(1). The usage of these algorithms is limited to networks on at most around 25 nodes mainly due to the space requirement. Here, we study space–time tradeoffs for finding ..."
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Cited by 11 (5 self)
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The fastest known exact algorithms for scorebased structure discovery in Bayesian networks on n nodes run in time and space 2 n n O(1). The usage of these algorithms is limited to networks on at most around 25 nodes mainly due to the space requirement. Here, we study space–time tradeoffs for finding an optimal network structure. When little space is available, we apply the Gurevich– Shelah recurrence—originally proposed for the Hamiltonian path problem—and obtain time 2 2n−s n O(1) in space 2 s n O(1) for any s = n/2,n/4,n/8,...; we assume the indegree of each node is bounded by a constant. For the more practical setting with moderate amounts of space, we present a novel scheme. It yields running time 2 n (3/2) p n O(1) in space 2 n (3/4) p n O(1) for any p = 0,1,...,n/2; these bounds hold as long as the indegrees are at most 0.238n. Furthermore, the latter scheme allows easy and efficient parallelization beyond previous algorithms. We also explore empirically the potential of the presented techniques. 1
Modeling Discrete Interventional Data using Directed Cyclic Graphical Models
"... We outline a representation for discrete multivariate distributions in terms of interventional potential functions that are globally normalized. This representation can be used to model the effects of interventions, and the independence properties encoded in this model can be represented as a direct ..."
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Cited by 9 (0 self)
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We outline a representation for discrete multivariate distributions in terms of interventional potential functions that are globally normalized. This representation can be used to model the effects of interventions, and the independence properties encoded in this model can be represented as a directed graph that allows cycles. In addition to discussing inference and sampling with this representation, we give an exponential family parametrization that allows parameter estimation to be stated as a convex optimization problem; we also give a convex relaxation of the task of simultaneous parameter and structure learning using group ℓ1regularization. The model is evaluated on simulated data and intracellular flow cytometry data. 1
Combining experiments to discover linear cyclic models with latent variables
 In AISTATS 2010
, 2010
"... We present an algorithm to infer causal relations between a set of measured variables on the basis of experiments on these variables. The algorithm assumes that the causal relations are linear, but is otherwise completely general: It provides consistent estimates when the true causal structure conta ..."
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Cited by 5 (2 self)
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We present an algorithm to infer causal relations between a set of measured variables on the basis of experiments on these variables. The algorithm assumes that the causal relations are linear, but is otherwise completely general: It provides consistent estimates when the true causal structure contains feedback loops and latent variables, while the experiments can involve surgical or ‘soft ’ interventions on one or multiple variables at a time. The algorithm is ‘online’ in the sense that it combines the results from any set of available experiments, can incorporate background knowledge and resolves conflicts that arise from combining results from different experiments. In addition we provide a necessary and sufficient condition that (i) determines when the algorithm can uniquely return the true graph, and (ii) can be used to select the next best experiment until this condition is satisfied. We demonstrate the method by applying it to simulated data and the flow cytometry data of Sachs et al (2005). 1
Modeling affordances using bayesian networks
 in IEEE  Intelligent Robotic Systems (IROS’06
, 2007
"... Abstract — Affordances represent the behavior of objects in terms of the robot’s motor and perceptual skills. This type of knowledge plays a crucial role in developmental robotic systems, since it is at the core of many higher level skills such as imitation. In this paper, we propose a general affor ..."
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Cited by 4 (1 self)
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Abstract — Affordances represent the behavior of objects in terms of the robot’s motor and perceptual skills. This type of knowledge plays a crucial role in developmental robotic systems, since it is at the core of many higher level skills such as imitation. In this paper, we propose a general affordance model based on Bayesian networks linking actions, object features and action effects. The network is learnt by the robot through interaction with the surrounding objects. The resulting probabilistic model is able to deal with uncertainty, redundancy and irrelevant information. We evaluate the approach using a real humanoid robot that interacts with objects. I.
Causal discovery for linear cyclic models with latent variables
"... We consider the problem of identifying the causal relationships among a set of variables in the presence of both feedback loops and unmeasured confounders. This is a challenging task which, for full identification, typically requires the use of randomized experiments. For linear systems, Eberhardt e ..."
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Cited by 3 (2 self)
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We consider the problem of identifying the causal relationships among a set of variables in the presence of both feedback loops and unmeasured confounders. This is a challenging task which, for full identification, typically requires the use of randomized experiments. For linear systems, Eberhardt et al (2010) recently provided a procedure for integrating data from several experiments, and gave a corresponding, but demanding, identifiability condition. In this paper we (i) characterize the underdetermination of the model when the identifiability condition is not fully satisfied, (ii) show that their algorithm is complete with regard to the search space and the assumptions, and (iii) extend the procedure to incorporate the common assumption of faithfulness, and any prior knowledge. The resulting method typically resolves much additional structure and often yields full identification with many fewer experiments. We demonstrate our procedure using simulated data, and apply it to the protein signaling dataset of Sachs et al (2005). 1
Learning linear cyclic causal models with latent variables. Submitted. Available online from the authors’ homepages
, 2012
"... Identifying causeeffect relationships between variables of interest is a central problem in science. Given a set of experiments we describe a procedure that identifies linear models that may contain cycles and latent variables. We provide a detailed description of the model family, full proofs of t ..."
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Cited by 1 (1 self)
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Identifying causeeffect relationships between variables of interest is a central problem in science. Given a set of experiments we describe a procedure that identifies linear models that may contain cycles and latent variables. We provide a detailed description of the model family, full proofs of the necessary and sufficient conditions for identifiability, a search algorithm that is complete, and a discussion of what can be done when the identifiability conditions are not satisfied. The algorithm is comprehensively tested in simulations, comparing it to competing algorithms in the literature. Furthermore, we adapt the procedure to the problem of cellular network inference, applying it to the biologically realistic data of the DREAM challenges. The paper provides a full theoretical foundation for the causal discovery procedure first presented by Eberhardt et al. (2010) and Hyttinen et al. (2010).
Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs
, 2012
"... The investigation of directed acyclic graphs (DAGs) encoding the same Markov property, that is the same conditional independence relations of multivariate observational distributions, has a long tradition; many algorithms exist for model selection and structure learning in Markov equivalence classes ..."
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Cited by 1 (0 self)
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The investigation of directed acyclic graphs (DAGs) encoding the same Markov property, that is the same conditional independence relations of multivariate observational distributions, has a long tradition; many algorithms exist for model selection and structure learning in Markov equivalence classes. In this paper, we extend the notion of Markov equivalence of DAGs to the case of interventional distributions arising from multiple intervention experiments. We show that under reasonable assumptions on the intervention experiments, interventional Markov equivalence defines a finer partitioning of DAGs than observational Markov equivalence and hence improves the identifiability of causal models. We give a graph theoretic criterion for two DAGs being Markov equivalent under interventions and show that each interventional Markov equivalence class can, analogously to the observational case, be uniquely represented by a chain graph called interventional essential graph (also known as CPDAG in the observational case). These are key insights for deriving a generalization of the Greedy Equivalence Search algorithm aimed at structure learning from interventional data. This new algorithm is evaluated in a simulation study.
Graph Reconstruction with DegreeConstrained Subgraphs
"... Given observations about a collection of nodes, the goal of graph reconstruction is to predict a set of edges that connect the nodes in a realistic fashion. A number of similar formulations of the problem have been introduced across research areas, notably social sciences, epidemiology and biology. ..."
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Given observations about a collection of nodes, the goal of graph reconstruction is to predict a set of edges that connect the nodes in a realistic fashion. A number of similar formulations of the problem have been introduced across research areas, notably social sciences, epidemiology and biology. What is common across domains is the understanding that interesting properties of the system are known to depend on the graph structure. As a example, consider the cell signaling network previously studied in [4, 2]. Gene expression modulations are a byproduct of a living network of activity. In this application, the expression levels of 11 signaling molecules have been measured using a technology called flow cytometry, and the goal is to recovery the causal influences of one molecule on another in this network. An important aspect of this data is the inclusion of interventions, whereby external factors are used to perturb the expressions of individual molecules, as these help to identify the directionality of influence between two molecules. The authors use Bayesian networks to infer the causal relations. In this framework, the expression levels of signaling molecules in a cell are modeled as discrete random variables whose dependency structure is assumed to form a directed acyclic graph. Then, using this wellestablished probabilistic