Author Archives: Juan-antonio Fernández-madrigal

Personalizing the assessments generated automatically for students in order to minimize plagiarism: the case of programming

June 30, 2017 10:41 ,

S. Manoharan, “Personalized Assessment as a Means to Mitigate Plagiarism,” in IEEE Transactions on Education, vol. 60, no. 2, pp. 112-119, May 2017.
DOI: 10.1109/TE.2016.2604210.

Although every educational institution has a code of academic honesty, they still encounter incidents of plagiarism. These are difficult and time-consuming to detect and deal with. This paper explores the use of personalized assessments with the goal of reducing incidents of plagiarism, proposing a personalized assessment software framework through which each student receives a unique problem set. The framework not only auto-generates the problem set but also auto-marks the solutions when submitted. The experience of using this framework is discussed, from the perspective of both students and staff, particularly with respect to its ability to mitigate plagiarism. A comparison of personalized and traditional assignments in the same class confirms that the former had far fewer observed plagiarism incidents. Although personalized assessment may not be cost-effective in all courses (such as language courses), it still can be effective in areas such as mathematics, engineering, science, and computing. This paper concludes that personalized assessment is a promising approach to counter plagiarism.

Posted in: Education , Tagged: ,

Reinforcement learning to learn the model of the world intrinsically motivated

June 27, 2017 12:10 ,

Todd Hester, Peter Stone, Intrinsically motivated model learning for developing curious robots, Artificial Intelligence, Volume 247, June 2017, Pages 170-186, ISSN 0004-3702, DOI: 10.1016/j.artint.2015.05.002.

Reinforcement Learning (RL) agents are typically deployed to learn a specific, concrete task based on a pre-defined reward function. However, in some cases an agent may be able to gain experience in the domain prior to being given a task. In such cases, intrinsic motivation can be used to enable the agent to learn a useful model of the environment that is likely to help it learn its eventual tasks more efficiently. This paradigm fits robots particularly well, as they need to learn about their own dynamics and affordances which can be applied to many different tasks. This article presents the texplore with Variance-And-Novelty-Intrinsic-Rewards algorithm (texplore-vanir), an intrinsically motivated model-based RL algorithm. The algorithm learns models of the transition dynamics of a domain using random forests. It calculates two different intrinsic motivations from this model: one to explore where the model is uncertain, and one to acquire novel experiences that the model has not yet been trained on. This article presents experiments demonstrating that the combination of these two intrinsic rewards enables the algorithm to learn an accurate model of a domain with no external rewards and that the learned model can be used afterward to perform tasks in the domain. While learning the model, the agent explores the domain in a developing and curious way, progressively learning more complex skills. In addition, the experiments show that combining the agent’s intrinsic rewards with external task rewards enables the agent to learn faster than using external rewards alone. We also present results demonstrating the applicability of this approach to learning on robots.

Posted in: Applications of reinforcement learning to robots, Artificial Intelligence , Tagged: ,

State of the art and historical background of the classical divergence between AI and robotics

June 27, 2017 12:03 ,

Kanna Rajan, Alessandro Saffiotti, Towards a science of integrated AI and Robotics, Artificial Intelligence, Volume 247, June 2017, Pages 1-9, ISSN 0004-3702, DOI: 10.1016/j.artint.2017.03.003.

The early promise of the impact of machine intelligence did not involve the partitioning of the nascent field of Artificial Intelligence. The founders of AI envisioned the notion of embedded intelligence as being conjoined between perception, reasoning and actuation. Yet over the years the fields of AI and Robotics drifted apart. Practitioners of AI focused on problems and algorithms abstracted from the real world. Roboticists, generally with a background in mechanical and electrical engineering, concentrated on sensori-motor functions. That divergence is slowly being bridged with the maturity of both fields and with the growing interest in autonomous systems. This special issue brings together the state of the art and practice of the emergent field of integrated AI and Robotics, and highlights the key areas along which this current evolution of machine intelligence is heading.

Posted in: Artificial Intelligence, Robotics , Tagged:

How “behaviour trees” generalize the subsumption architecture and some other control architecture frameworks

June 27, 2017 11:39 ,

M. Colledanchise and P. Ögren, “How Behavior Trees Modularize Hybrid Control Systems and Generalize Sequential Behavior Compositions, the Subsumption Architecture, and Decision Trees,” in IEEE Transactions on Robotics, vol. 33, no. 2, pp. 372-389, April 2017.DOI: 10.1109/TRO.2016.2633567.

Behavior trees (BTs) are a way of organizing the switching structure of a hybrid dynamical system (HDS), which was originally introduced in the computer game programming community. In this paper, we analyze how the BT representation increases the modularity of an HDS and how key system properties are preserved over compositions of such systems, in terms of combining two BTs into a larger one. We also show how BTs can be seen as a generalization of sequential behavior compositions, the subsumption architecture, and decisions trees. These three tools are powerful but quite different, and the fact that they are unified in a natural way in BTs might be a reason for their popularity in the gaming community. We conclude the paper by giving a set of examples illustrating how the proposed analysis tools can be applied to robot control BTs.

Posted in: Robotic architectures , Tagged: ,

Modelling hierarchical stochastic signals (i.e., decomposable into sub-signals hierarchichally)

June 27, 2017 11:17 ,

Truyen Tran, Dinh Phung, Hung Bui, Svetha Venkatesh, Hierarchical semi-Markov conditional random fields for deep recursive sequential data, Artificial Intelligence, Volume 246, May 2017, Pages 53-85, ISSN 0004-3702, DOI: 10.1016/j.artint.2017.02.003.

We present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of linear-chain conditional random fields to model deep nested Markov processes. It is parameterised as a conditional log-linear model and has polynomial time algorithms for learning and inference. We derive algorithms for partially-supervised learning and constrained inference. We develop numerical scaling procedures that handle the overflow problem. We show that when depth is two, the HSCRF can be reduced to the semi-Markov conditional random fields. Finally, we demonstrate the HSCRF on two applications: (i) recognising human activities of daily living (ADLs) from indoor surveillance cameras, and (ii) noun-phrase chunking. The HSCRF is capable of learning rich hierarchical models with reasonable accuracy in both fully and partially observed data cases.

Posted in: Artificial Intelligence, Probability and statistics , Tagged: , , ,

A summary of the Clarion cognitive architecture

June 27, 2017 11:07 ,

Ron Sun, Anatomy of the Mind: a Quick Overview, Cognitive Computation, February 2017, Volume 9, Issue 1, pp 1–4, DOI: 10.1007/s12559-016-9444-2.

The recently published book, “Anatomy of the Mind,” explains psychological (cognitive) mechanisms, processes, and functionalities through a comprehensive computational theory of the human mind—that is, a cognitive architecture. The goal of the work has been to develop a unified framework and then to develop process-based mechanistic understanding of psychological phenomena within the unified framework. In this article, I will provide a quick overview of the work.

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

How very simple digital signal processing techniques, such as numerical filtering and linear interpolation, may provide PDF estimates with improved statistical properties over the histogram and close to, or better than, what can be obtained using Kernel based estimators

June 27, 2017 10:48 ,

P. Carbone, D. Petri and K. Barbé, “Nonparametric Probability Density Estimation via Interpolation Filtering,” in IEEE Transactions on Instrumentation and Measurement, vol. 66, no. 4, pp. 681-690, April 2017.DOI: 10.1109/TIM.2017.2657398.

In this paper, we discuss nonparametric estimation of the probability density function (PDF) of a univariate random variable. This problem has been the subject of a vast amount of scientific literature in many domains, while statisticians are mainly interested in the analysis of the properties of proposed estimators, and engineers treat the histogram as a ready-to-use tool for a data set analysis. By considering histogram data as a numerical sequence, a simple approach for PDF estimation is presented in this paper. It is based on basic notions related to the reconstruction of a continuous-time signal from a sequence of samples. When estimating continuous PDFs, it is shown that the proposed approach is as accurate as kernel-based estimators, widely adopted in the statistical literature. Conversely, it can provide better accuracy when the PDF to be estimated exhibits a discontinuous behavior. The main statistical properties of the proposed estimators are derived and then verified by simulations related to the common cases of normal and uniform density functions. The obtained results are also used to derive optimal, i.e., minimum integral of the mean square error, estimators.

Posted in: Probability and statistics , Tagged: ,

On the current limitations of robotics research concerning the generalization of reported results to different set-ups

June 27, 2017 09:11 ,

Francesco Amigoni, Matteo Luperto, Viola Schiaffonati,Toward generalization of experimental results for autonomous robots, Robotics and Autonomous Systems, Volume 90, April 2017, Pages 4-14, ISSN 0921-8890, DOI: 10.1016/j.robot.2016.08.016.

In this paper we discuss some issues in the experimental evaluation of intelligent autonomous systems, focusing on systems, like autonomous robots, operating in physical environments. We argue that one of the weaknesses of current experimental practices is the low degree of generalization of experimental results, meaning that knowing the performance a robot system obtains in a test setting does not provide much information about the performance the same system could achieve in other settings. We claim that one of the main obstacles to achieve generalization of experimental results in autonomous robotics is the low degree of representativeness of the selected experimental settings. We survey and discuss the degree of representativeness of experimental settings used in a significant sample of current research and we propose some strategies to overcome the emerging limitations.

Posted in: Robotics , Tagged:

Robots that pre-compute a number of possible behaviours (in simulation) and then learn their performance with them (propragating that performance measures to similar behaviors through Gaussian Processes Regression) and select the best at each situation (through Bayesian Optimization), thus confronting varying environments and damages to the robot

June 27, 2017 09:02 ,

A. Cully, et al. Robots that can adapt like animals, Nature, 521 (2015), pp. 503–507, DOI: 10.1038/nature14422.

Robots have transformed many industries, most notably manufacturing, and have the power to deliver tremendous benefits to society, such as in search and rescue, disaster response, health care and transportation. They are also invaluable tools for scientific exploration in environments inaccessible to humans, from distant planets to deep oceans. A major obstacle to their widespread adoption in more complex environments outside factories is their fragility. Whereas animals can quickly adapt to injuries, current robots cannot think outside the box to find a compensatory behaviour when they are damaged: they are limited to their pre-specified self-sensing abilities, can diagnose only anticipated failure modes, and require a pre-programmed contingency plan for every type of potential damage, an impracticality for complex robots. A promising approach to reducing robot fragility involves having robots learn appropriate behaviours in response to damage, but current techniques are slow even with small, constrained search spaces. Here we introduce an intelligent trial-and-error algorithm that allows robots to adapt to damage in less than two minutes in large search spaces without requiring self-diagnosis or pre-specified contingency plans. Before the robot is deployed, it uses a novel technique to create a detailed map of the space of high-performing behaviours. This map represents the robotâ €™ s prior knowledge about what behaviours it can perform and their value. When the robot is damaged, it uses this prior knowledge to guide a trial-and-error learning algorithm that conducts intelligent experiments to rapidly discover a behaviour that compensates for the damage. Experiments reveal successful adaptations for a legged robot injured in five different ways, including damaged, broken, and missing legs, and for a robotic arm with joints broken in 14 different ways. This new algorithm will enable more robust, effective, autonomous robots, and may shed light on the principles that animals use to adapt to injury.

Posted in: Robotics , Tagged: , ,

How to improve statistical results obtained from limited set-ups through active sampling, and a nice review of possible pitfalls in conducting statistical research (and a mention to “pre-registration” of hypothesis and plans to be peer-reviewed before submitting the results)

June 27, 2017 08:42 ,

Romy Lorenz, Adam Hampshire, Robert Leech, Neuroadaptive Bayesian Optimization and Hypothesis Testing, Trends in Cognitive Sciences, Volume 21, Issue 3, March 2017, Pages 155-167, ISSN 1364-6613, DOI: 10.1016/j.tics.2017.01.006.

Cognitive neuroscientists are often interested in broad research questions, yet use overly narrow experimental designs by considering only a small subset of possible experimental conditions. This limits the generalizability and reproducibility of many research findings. Here, we propose an alternative approach that resolves these problems by taking advantage of recent developments in real-time data analysis and machine learning. Neuroadaptive Bayesian optimization is a powerful strategy to efficiently explore more experimental conditions than is currently possible with standard methodology. We argue that such an approach could broaden the hypotheses considered in cognitive science, improving the generalizability of findings. In addition, Bayesian optimization can be combined with preregistration to cover exploration, mitigating researcher bias more broadly and improving reproducibility.

Posted in: Cognitive sciences, Probability and statistics , Tagged: ,