Example of learning a Bayesian network using expert knowledge

October 5, 2017 08:55 ,

H. Amirkhani, M. Rahmati, P. J. F. Lucas and A. Hommersom, Exploiting Experts’ Knowledge for Structure Learning of Bayesian Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 11, pp. 2154-2170, DOI: 10.1109/TPAMI.2016.2636828.

Learning Bayesian network structures from data is known to be hard, mainly because the number of candidate graphs is super-exponential in the number of variables. Furthermore, using observational data alone, the true causal graph is not discernible from other graphs that model the same set of conditional independencies. In this paper, it is investigated whether Bayesian network structure learning can be improved by exploiting the opinions of multiple domain experts regarding cause-effect relationships. In practice, experts have different individual probabilities of correctly labeling the inclusion or exclusion of edges in the structure. The accuracy of each expert is modeled by three parameters. Two new scoring functions are introduced that score each candidate graph based on the data and experts’ opinions, taking into account their accuracy parameters. In the first scoring function, the experts’ accuracies are estimated using an expectation-maximization-based algorithm and the estimated accuracies are explicitly used in the scoring process. The second function marginalizes out the accuracy parameters to obtain more robust scores when it is not possible to obtain a good estimate of experts’ accuracies. The experimental results on simulated and real world datasets show that exploiting experts’ knowledge can improve the structure learning if we take the experts’ accuracies into account.

Posted in: Probability and statistics , Tagged: , ,

Dealing with nonlinearities in Kalman filters through Monte Carlo modelling for minimizing divergence

October 5, 2017 08:51 ,

S. Gultekin and J. Paisley, Nonlinear Kalman Filtering With Divergence Minimization, IEEE Transactions on Signal Processing, vol. 65, no. 23, pp. 6319-6331, DOI: 10.1109/TSP.2017.2752729.

We consider the nonlinear Kalman filtering problem using Kullback-Leibler (KL) and α-divergence measures as optimization criteria. Unlike linear Kalman filters, nonlinear Kalman filters do not have closed form Gaussian posteriors because of a lack of conjugacy due to the nonlinearity in the likelihood. In this paper, we propose novel algorithms to approximate this posterior by optimizing the forward and reverse forms of the KL divergence, as well as the α-divergence that contains these two as limiting cases. Unlike previous approaches, our algorithms do not make approximations to the divergences being optimized, but use Monte Carlo techniques to derive unbiased algorithms for direct optimization. We assess performance on radar and sensor tracking, and options pricing, showing general improvement over the extended, unscented, and ensemble Kalman filters, as well as competitive performance with particle filtering.

Posted in: Bayesian filtering , Tagged:

Empirical evidence of the negative correlation between cognitive workload and attention in humans

October 5, 2017 07:54 ,

Kyle J. Jaquess, Rodolphe J. Gentili, Li-Chuan Lo, Hyuk Oh, Jing Zhang, Jeremy C. Rietschel, Matthew W. Miller, Ying Ying Tan, Bradley D. Hatfield, Empirical evidence for the relationship between cognitive workload and attentional reserve, International Journal of Psychophysiology, Volume 121, 2017, Pages 46-55, DOI: 10.1016/j.ijpsycho.2017.09.007.

While the concepts of cognitive workload and attentional reserve have been thought to have an inverse relationship for some time, such a relationship has never been empirically tested. This was the purpose of the present study. Aspects of the electroencephalogram were used to assess both cognitive workload and attentional reserve. Specifically, spectral measures of cortical activation were used to assess cognitive workload, while amplitudes of the event-related potential from the presentation of unattended “novel” sounds were used to assess attentional reserve. The relationship between these two families of measures was assessed using canonical correlation. Twenty-seven participants performed a flight simulator task under three levels of challenge. Verification of manipulation was performed using self-report measures of task demand, objective task performance, and heart rate variability using electrocardiography. Results revealed a strong, negative relationship between the spectral measures of cortical activation, believed to be representative of cognitive workload, and ERP amplitudes, believed to be representative of attentional reserve. This finding provides support for the theoretical and intuitive notion that cognitive workload and attentional reserve are inversely related. The practical implications of this result include improved state classification using advanced machine learning techniques, enhanced personnel selection/recruitment/placement, and augmented learning/training.

Posted in: Psycho-physiological bases of engineering , Tagged:

Survey on visual attention in 3D for robotics

September 23, 2017 08:42 ,

Ekaterina Potapova, Michael Zillich, and Markus Vincze, Survey of recent advances in 3D visual attention for robotics, The International Journal of Robotics Research, Vol 36, Issue 11, pp. 1159 – 1176, DOI: 10.1177/0278364917726587.

3D visual attention plays an important role in both human and robotics perception that yet has to be explored in full detail. However, the majority of computer vision and robotics methods are concerned only with 2D visual attention. This survey presents findings and approaches that cover 3D visual attention in both human and robot vision, summarizing the last 30 years of research and also looking beyond computational methods. First, we present work in such fields as biological vision and neurophysiology, studying 3D attention in human observers. This provides a view of the role attention plays at the system level for biological vision. Then, we cover computer and robot vision approaches that take 3D visual attention into account. We compare approaches with respect to different categories, such as feature-based, data-based, or depth-based visual attention, and draw conclusions on what advances will help robotics to cope better with complex real-world settings and tasks.

Posted in: Computer vision, Robot sensors , Tagged:

On how humans run simulations for reasoning about physics

September 20, 2017 08:38 ,

James R. Kubricht, Keith J. Holyoak, Hongjing Lu, Intuitive Physics: Current Research and Controversies, Trends in Cognitive Sciences, Volume 21, Issue 10, 2017, Pages 749-759, DOI: 10.1016/j.tics.2017.06.002.

Early research in the field of intuitive physics provided extensive evidence that humans succumb to common misconceptions and biases when predicting, judging, and explaining activity in the physical world. Recent work has demonstrated that, across a diverse range of situations, some biases can be explained by the application of normative physical principles to noisy perceptual inputs. However, it remains unclear how knowledge of physical principles is learned, represented, and applied to novel situations. In this review we discuss theoretical advances from heuristic models to knowledge-based, probabilistic simulation models, as well as recent deep-learning models. We also consider how recent work may be reconciled with earlier findings that favored heuristic models.

Posted in: Psycho-physiological bases of engineering

On the roots in the ability to control outcomes of human motivation

September 20, 2017 08:35 ,

Justin M. Moscarello, Catherine A. Hartley, Agency and the Calibration of Motivated Behavior, Trends in Cognitive Sciences, Volume 21, Issue 10, 2017, Pages 725-735, DOI: 10.1016/j.tics.2017.06.008.

The controllability of positive or negative environmental events has long been recognized as a critical factor determining their impact on an organism. In studies across species, controllable and uncontrollable reinforcement have been found to yield divergent effects on subsequent behavior. Here we present a model of the organizing influence of control, or a lack thereof, on the behavioral repertoire. We propose that individuals derive a generalizable estimate of agency from controllable and uncontrollable outcomes, which serves to calibrate their behavioral strategies in a manner that is most likely to be adaptive given their prior experience.

Posted in: Psycho-physiological bases of engineering , Tagged:

A novel method for hard real-time communications using the physical layer of Ethernet and a variation of TDMA

September 20, 2017 08:30 ,

Andrzej Przybył, Hard real-time communication solution for mechatronic systems, Robotics and Computer-Integrated Manufacturing, Volume 49, 2018, Pages 309-316, DOI: 10.1016/j.rcim.2017.08.001.

The paper proposes a method to build a highly efficient real-time communication solution for mechatronic systems. The method is based on the Ethernet physical layer (PHY) and on field programmable gate array (FPGA) technology and offers a better performance when compared to commercially available communication solutions. Although it is not directly compatible with the OSI/ISO model of TCP/IP protocol, vertical integration is done with a gateway. This provides simplicity and safety. Moreover, the use of the FPGA allows for integrating the communication solution with the user algorithm of particular distributed device inside a single chip. Therefore, the proposed solution is efficient and highly integrated.

Posted in: Communication networks, Real-Time Systems , Tagged:

Calibrating a robotic manipulator through photogrammetry, and a nice state-of-the-art in the issue of robot calibration

September 20, 2017 08:03 ,

Alexandre Filion, Ahmed Joubair, Antoine S. Tahan, Ilian A. Bonev, Robot calibration using a portable photogrammetry system, Robotics and Computer-Integrated Manufacturing, Volume 49, 2018, Pages 77-87, DOI: 10.1016/j.rcim.2017.05.004.

This work investigates the potential use of a commercially-available portablephotogrammetry system (the MaxSHOT 3D) in industrial robot calibration. To demonstrate the effectiveness of this system, we take the approach of comparing the device with a laser tracker (the FARO laser tracker) by calibrating an industrial robot, with each device in turn, then comparing the obtained robot position accuracy after calibration. As the use of a portablephotogrammetry system in robot calibration is uncommon, this paper presents how to proceed. It will cover the theory of robot calibration: the robot’s forward and inverse kinematics, the elasto-geometrical model of the robot, the generation and ultimate selection of robot configurations to be measured, and the parameter identification. Furthermore, an experimental comparison of the laser tracker and the MaxSHOT3D is described. The obtained results show that the FARO laser trackerION performs slightly better: The absolute positional accuracy obtained with the laser tracker is 0.365mm and 0.147mm for the maximum and the mean position errors, respectively. Nevertheless, the results obtained by using the MaxSHOT3D are almost as good as those obtained by using the laser tracker: 0.469mm and 0.197mm for the maximum and the mean position errors, respectively. Performances in distance accuracy, after calibration (i.e. maximum errors), are respectively 0.329mm and 0.352mm, for the laser tracker and the MaxSHOT 3D. However, as the validation measurements were acquired with the laser tracker, bias favors this device. Thus, we may conclude that the calibration performances of the two measurement devices are very similar.

Posted in: Industrial robots, Robot models , Tagged: ,

Interesting implementation of visual graph SLAM in C++ for educational purposes

September 19, 2017 07:59 ,

Dominik Schlegel, Mirco Colosi, Giorgio Grisetti, ProSLAM: Graph SLAM from a Programmer’s Perspective/strong>, arXiv:1709.04377.

In this paper we present ProSLAM, a lightweight stereo visual SLAM system designed with simplicity in mind. Our work stems from the experience gathered by the authors while teaching SLAM to students and aims at providing a highly modular system that can be easily implemented and understood. Rather than focusing on the well known mathematical aspects of Stereo Visual SLAM, in this work we highlight the data structures and the algorithmic aspects that one needs to tackle during the design of such a system. We implemented ProSLAM using the C++ programming language in combination with a minimal set of well known used external libraries. In addition to an open source implementation, we provide several code snippets that address the core aspects of our approach directly in this paper. The results of a thorough validation performed on standard benchmark datasets show that our approach achieves accuracy comparable to state of the art methods, while requiring substantially less computational resources.

Posted in: Computer vision, Mobile robot SLAM , Tagged: , , ,

Improving efficiency of decision with POMDPs in high-dimension state spaces

September 19, 2017 07:45 ,

Dmitry Kopitkov and Vadim Indelman, No belief propagation required: Belief space planning in high-dimensional state spaces via factor graphs, the matrix determinant lemma, and re-use of calculation, The International Journal of Robotics Research, Vol 36, Issue 10, pp. 1088 – 1130, DOI: 10.1177/0278364917721629.

We develop a computationally efficient approach for evaluating the information-theoretic term within belief space planning (BSP), where during belief propagation the state vector can be constant or augmented. We consider both unfocused and focused problem settings, whereas uncertainty reduction of the entire system or only of chosen variables is of interest, respectively. State-of-the-art approaches typically propagate the belief state, for each candidate action, through calculation of the posterior information (or covariance) matrix and subsequently compute its determinant (required for entropy). In contrast, our approach reduces runtime complexity by avoiding these calculations. We formulate the problem in terms of factor graphs and show that belief propagation is not needed, requiring instead a one-time calculation that depends on (the increasing with time) state dimensionality, and per-candidate calculations that are independent of the latter. To that end, we develop an augmented version of the matrix determinant lemma, and show that computations can be re-used when evaluating impact of different candidate actions. These two key ingredients and the factor graph representation of the problem result in a computationally efficient (augmented) BSP approach that accounts for different sources of uncertainty and can be used with various sensing modalities. We examine the unfocused and focused instances of our approach, and compare it with the state of the art, in simulation and using real-world data, considering problems such as autonomous navigation in unknown environments, measurement selection and sensor deployment. We show that our approach significantly reduces running time without any compromise in performance.

Posted in: Robot task planning , Tagged: