Bayesian Robot Programming

Lebeltel, Olivier; Bessìère, Pierre; Diard, Julien; Mazer, Emmanuel

doi:10.1023/b:auro.0000008671.38949.43

Cited by 114 publications

(107 citation statements)

References 68 publications

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“…According to the Bayesian Programming (BP) approach [13,17,23] BAD models [7,8] are a special type of Bayesian Networks (BN) [18 -22] using concepts from probabilistic robotics [11].…”

Section: Related Workmentioning

confidence: 99%

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Further Steps towards Driver Modeling According to the Bayesian Programming Approach

Möbus

Eilers

2009

Digital Human Modeling

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Abstract. The Human Centered Design (HCD) of Partial Autonomous Driver Assistance Systems (PADAS) requires Digital Human Models (DHMs) of human control strategies for simulating traffic scenarios. We describe first results to model lateral and longitudinal control behavior of drivers with simple dynamic Bayesian sensory-motor models according to the Bayesian Programming (BP) approach: Bayesian Autonomous Driver (BAD) models. BAD models are learnt from multivariate time series of driving episodes generated by single or groups of users. The variables of the time series describe phenomena and processes of perception, cognition, and action control of drivers. BAD models reconstruct the joint probability distribution (JPD) of those variables by a composition of conditional probability distributions (CPDs). The real-time control of virtual vehicles is achieved by inferring the appropriate actions under the evidence of sensory percepts with the help of the reconstructed JPD.Keywords: digital human response models, driver models, Bayesian autonomous driver models, learning of human control strategies, probabilistic Bayesian lateral and longitudinal control, graphical modeling, human behavior learning and transfer, Bayesian Programming.

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“…According to the Bayesian Programming (BP) approach [13,17,23] BAD models [7,8] are a special type of Bayesian Networks (BN) [18 -22] using concepts from probabilistic robotics [11].…”

Section: Related Workmentioning

confidence: 99%

“…In presenting basic concepts of BP like postulates, definitions, notations and rules we borrow from [13,17]. A BP is defined as a mean of specifying a family of probability distributions.…”

Section: Basic Conceptsmentioning

confidence: 99%

Further Steps towards Driver Modeling According to the Bayesian Programming Approach

Möbus

Eilers

2009

Digital Human Modeling

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“…The approach presented in this report also draws on the vast amount of work done in the area of clustering, a good survey of which is presented in [22]. Additionally, this work attempts to be fully probabilistic and is grounded on a Bayesian Programming methodology as described in [23].…”

Section: Related Workmentioning

confidence: 99%

“…Elaborate details on the concept, its structure and its semantics are available in [23]. The complete Bayesian program used to do the learning and inference processes in this work, is summarized as shown in figure 2.…”

Section: The Concept Model and Conceptualizationmentioning

confidence: 99%

A bayesian conceptualization of space for mobile robots

Vasudevan¹,

Siegwart²

2007

2007 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-The future of robots, as our companions is dependent on their ability to understand, interpret and represent the environment in a human compatible manner. Towards this aim of making robots more spatially cognizant, the presented work is part of an attempt to create a hierarchical probabilistic concept-oriented representation of space, based on objects. Specifically, this work details efforts taken towards learning and generating concepts from the perceived objects and attempts to classify places using the concepts gleaned. The approach is based on learning from exemplars, clustering and the use of Bayesian network classifiers. Experiments on conceptualization and place classification are reported. Thus, the theme of the work is -conceptualization and classification for representation and spatial cognition.

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“…The main area where BP has been and continues to be applied is robotics [2], [1], [3], [4], [5]. This paper is divided into five parts.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Exploration in Life Science Informatics

López¹,

Benfenati²,

Dubitzky³

2004

Lecture Notes in Computer Science

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Abstract. This paper shows an application of Bayesian Programming to model a simple artificial life problem: that of a worm trying to live in a world full of poison. Any model of a real phenomenon is incomplete because there will always exist unknown, hidden variables that influence the phenomenon. To solve this problem we apply a new formalism, Bayesian programming, which has previously been used in autonomous robot programming. The proposed worm model has been used to train a population of worms using genetic algorithms. We will see the advantages of our method compared with a classical approach. Finally, we discuss the emergent behaviour patterns we observed in some of the worms and conclude by explaining the advantages of the applied method.

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Bayesian Robot Programming

Cited by 114 publications

References 68 publications

Further Steps towards Driver Modeling According to the Bayesian Programming Approach

Further Steps towards Driver Modeling According to the Bayesian Programming Approach

A bayesian conceptualization of space for mobile robots

Knowledge Exploration in Life Science Informatics

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