An Artificial Synaptic Plasticity Mechanism for Classical Conditioning with Neural Networks

We present an artificial synaptic plasticity (ASP) mechanism that allows artificial systems to make associations between environmental stimuli and learn new skills at runtime. ASP builds on the classical neural network for simulating associative learning, which is induced through a conditioning-like procedure. Experiments in a simulated mobile robot demonstrate that ASP has successfully generated conditioned responses. The robot has learned during environmental exploration to use sensors added after training, improving its object-avoidance capabilities

SAFEL - A Situation-aware Fear Learning Model

This thesis proposes a novel and robust online adaptation mechanism for threat prediction and prevention capable of taking into consideration complex contextual and temporal information in its internal learning processes. The proposed mechanism is a hybrid cognitive computational model named SAFEL (Situation-Aware FEar Learning), which integrates machine learning algorithms with concepts of situation-awareness from expert systems to simulate both the cued and contextual fear-conditioning phenomena. SAFEL is inspired by well-known neuroscience findings on the brain's mechanisms of fear learning and memory to provide autonomous robots with the ability to predict undesirable or threatening situations to themselves. SAFEL's ultimate goal is to allow autonomous robots to perceive intricate elements and relationships in their environment, learn with experience through autonomous environmental exploration, and adapt at execution time to environmental changes and threats. SAFEL consists of a hybrid architecture composed of three modules, each based on a different approach and inspired by a different region (or function) of the brain involved in fear learning. These modules are: the Amygdala Module (AM), the Hippocampus Module (HM) and the Working Memory Module (WMM). The AM learns and detects environmental threats while the HM makes sense of the robot's context. The WMM is responsible for combining and associating the two types of information processed by the AM and HM. More specifically, the AM simulates the cued conditioning phenomenon by creating associations between co-occurring aversive and neutral environmental stimuli. The AM represents the kernel of emotional appraisal and threat detection in SAFEL's architecture. The HM, in turn, handles environmental information at a higher level of abstraction and complexity than the AM, which depicts the robot's situation as a whole. The information managed by the HM embeds in a unified representation the temporal interactions of multiple stimuli in the environment. Finally, the WMM simulates the contextual conditioning phenomenon by creating associations between the contextual memory formed in the HM and the emotional memory formed in the AM, thus giving emotional meaning to the contextual information acquired in past experiences. Ultimately, any previously experienced pattern of contextual information triggers the retrieval of that stored contextual memory and its emotional meaning from the WMM, warning the robot that an undesirable situation is likely to happen in the near future. The main contribution of this work as compared to the state of the art is a domain-independent mechanism for online learning and adaptation that combines a fear-learning model with the concept of temporal context and is focused on real-world applications for autonomous robotics. SAFEL successfully integrates a symbolic rule-based paradigm for situation management with machine learning algorithms for memorizing and predicting environmental threats to the robot based on complex temporal context. SAFEL has been evaluated in several experiments, which analysed the performance of each module separately. Ultimately, we conducted a comprehensive case study in the robot soccer scenario to evaluate the collective work of all modules as a whole. This case study also analyses to which extent the emotional feedback of SAFEL can improve the intelligent behaviour of a robot in a practical real-world situation, where adaptive skills and fast/flexible decision-making are crucial

CraftContext: A Test Platform for Context-Aware Applications

This paper presents a tool, called CraftContext, capable of leveraging the test phase of contextaware application development. CraftContext offers a 3D simulation environment, which is rich in details and resources, and is accessed by a robust and portable CORBA-based API. CraftContext excels most currentexisting testing tools due to its adaptability to different domains.Key words: context-awareness, test tool, CraftContext, JacORB, CORBA, minecraft

A Situation-Aware Fear Learning (SAFEL) Model for Robots

This work proposes a novel Situation-Aware FEar Learning (SAFEL) model for robots. SAFEL combines concepts of situation-aware expert systems with well-known neuroscientific findings on the brain fear-learning mechanism to allow companion robots to predict undesirable or threatening situations based on past experiences. One of the main objectives is to allow robots to learn complex temporal patterns of sensed environmental stimuli and create a representation of these patterns. This memory can be later associated with a negative or positive “emotion”, analogous to fear and confidence. Experiments with a real robot demonstrated SAFEL’s success in generating contextual fear conditioning behaviour with predictive capabilities based on situational information

Synthesis of tin nanocrystals in room temperature ionic liquids

International audienceThe aim of this work was to investigate the synthesis of tin nanoparticles (NPs) or tin/carbon composites, in room temperature ionic liquids (RTILs), that could be used as structured anode materials for Li-ion batteries. An innovative route for the synthesis of Sn nanoparticles in such media is successfully developed. Compositions, structures, sizes and morphologies of NPs were characterized by high-energy X-ray diffraction (HEXRD), X-ray photoelectron spectroscopy (XPS) and high-resolution transmission electron microscopy (HRTEM). Our findings indicated that (i) metallic tetragonal β-Sn was obtained and (ii) the particle size could be tailored by tuning the nature of the RTILs, leading to nano-sized spherical particles with a diameter ranging from 3 to 10 nm depending on synthesis conditions. In order to investigate carbon composite materials for Li-ion batteries, Sn nanoparticles were successfully deposited on the surface of multi-wall carbon nanotubes (MWCNT). Moreover, electrochemical properties have been studied in relation to a structural study of the nanocomposites. The poor electrochemical performances as a negative electrode in Li-ion batteries is due to a significant amount of RTIL trapped within the pores of the nanotubes as revealed by XPS investigations. This dramatically affected the gravimetric capacity of the composites and limited the diffusion of lithium. The findings of this work however offer valuable insights into the exciting possibilities for synthesis of novel nano-sized particles and/or alloys (e.g. Sn-Cu, Sn-Co, Sn-Ni, etc.) and the importance of carbon morphology in metal pulverization during the alloying/dealloying process as well as prevention of ionic liquid trapping