International audience

International audience

RLLib is a lightweight C++ template library that implements incremental, standard, and gradient temporal-difference learning algorithms in reinforcement learning. It is an optimized library for robotic applications and embedded devices that operates under fast duty cycles (e.g., $$\le $$ 30 ms). RLLib has been tested and evaluated on RoboCup 3D soccer simulation agents, NAO V4 humanoid robots, and Tiva C series launchpad microcontrollers to predict, control, learn behavior, and represent learnable knowledge.

We investigate the decision-making and behavior of robotic biped goalkeepers, applied to the RoboCup 3D Soccer Simulation League. We introduce two approaches to the goalkeeper’s behavior: first a heuristics-based approach that uses linear regression and Kalman filters for improved perception, and another based on mental models which uses nonlinear regression for ball trajectory filtering. Our experiments consist of 30,000 kick-and-save tests, using 100 random angle and distance kicks from six distance categories and four angle categories repeated 30 times. Our benchmark results show that both proposed approaches bring significant improvements for the goalkeeper’s save success rates ( \(>\) 200 %) and validate the applicability of the novel mental model based decision-making process. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

BACKGROUND: High-throughput screening (HTS) is one of the main strategies to identify novel entry points for the development of small molecule chemical probes and drugs and is now commonly accessible to public sector research. Large amounts of data generated in HTS campaigns are submitted to public repositories such as PubChem, which is growing at an exponential rate. The diversity and quantity of available HTS assays and screening results pose enormous challenges to organizing, standardizing, integrating, and analyzing the datasets and thus to maximize the scientific and ultimately the public health impact of the huge investments made to implement public sector HTS capabilities. Novel approaches to organize, standardize and access HTS data are required to address these challenges. RESULTS: We developed the first ontology to describe HTS experiments and screening results using expressive description logic. The BioAssay Ontology (BAO) serves as a foundation for the standardization of HTS assays and data and as a semantic knowledge model. In this paper we show important examples of formalizing HTS domain knowledge and we point out the advantages of this approach. The ontology is available online at the NCBO bioportal http://bioportal.bioontology.org/ontologies/44531. CONCLUSIONS: After a large manual curation effort, we loaded BAO-mapped data triples into a RDF database store and used a reasoner in several case studies to demonstrate the benefits of formalized domain knowledge representation in BAO. The examples illustrate semantic querying capabilities where BAO enables the retrieval of inferred search results that are relevant to a given query, but are not explicitly defined. BAO thus opens new functionality for annotating, querying, and analyzing HTS datasets and the potential for discovering new knowledge by means of inference.

We present an approach for recognition and subsequent prediction of spatio-temporal patterns in a physical real-time environment. The motivation is to provide a domain-independent approach for the analysis of agent's behavior in adversarial multi-agent scenarios. The goal is to create an opponent- specific model, which is used for behavior prediction. We develop a framework for representing a set of hierarchically structured facts, events and actions using temporal logic. Recognition, learning, and prediction is performed using a probabilistic approach utilizing Bayesian Networks. The system is applied to the domain of the RoboCup 3D Simulation League and evaluated with regard to the recognition-, prediction- and realtime capabilities.