43 research outputs found
A review on probabilistic graphical models in evolutionary computation
Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Evolutionary algorithms is one such discipline that has employed probabilistic graphical models to improve the search for optimal solutions in complex problems. This paper shows how probabilistic graphical models have been used in evolutionary algorithms to improve their performance in solving complex problems. Specifically, we give a survey of probabilistic model building-based evolutionary algorithms, called estimation of distribution algorithms, and compare different methods for probabilistic modeling in these algorithms
Review of the techniques used in motorâcognitive humanârobot skill transfer
Abstract A conventional robot programming method extensively limits the reusability of skills in the developmental aspect. Engineers programme a robot in a targeted manner for the realisation of predefined skills. The low reusability of generalâpurpose robot skills is mainly reflected in inability in novel and complex scenarios. Skill transfer aims to transfer human skills to generalâpurpose manipulators or mobile robots to replicate humanâlike behaviours. Skill transfer methods that are commonly used at present, such as learning from demonstrated (LfD) or imitation learning, endow the robot with the expert's lowâlevel motor and highâlevel decisionâmaking ability, so that skills can be reproduced and generalised according to perceived context. The improvement of robot cognition usually relates to an improvement in the autonomous highâlevel decisionâmaking ability. Based on the idea of establishing a generic or specialised robot skill library, robots are expected to autonomously reason about the needs for using skills and plan compound movements according to sensory input. In recent years, in this area, many successful studies have demonstrated their effectiveness. Herein, a detailed review is provided on the transferring techniques of skills, applications, advancements, and limitations, especially in the LfD. Future research directions are also suggested
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Multiplicative updates for large margin classifiers
Abstract. Various problems in nonnegative quadratic programming arise in the training of large margin classifiers. We derive multiplicative updates for these problems that converge monotonically to the desired solutions for hard and soft margin classifiers. The updates differ strikingly in form from other multiplicative updates used in machine learning. In this paper, we provide complete proofs of convergence for these updates and extend previous work to incorporate sum and box constraints in addition to nonnegativity.