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

Improving the predictive performance of SAFEL: A Situation-Aware FEar Learning model

In this paper, we optimize the predictive performance of a Situation-Aware FEar Learning model (SAFEL) by investigating the relationship between its parameters. SAFEL is a hybrid computational model based on the fear-learning system of the brain, which was developed to provide robots with the capability to predict threatening or undesirable situations based on temporal context. The main aim of this work is to improve SAFEL's emotional response. An emotional response coherent with environmental changes is essential not only for self-preservation and adaptation purposes, but also for improving the believability and interaction skills of companion robots. Experiments with a NAO humanoid robot show that adjusting the ratio between two parameters of SAFEL can significantly increase the predictive performance and reduce parameter settings

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

Fear Learning for Flexible Decision Making in RoboCup: A Discussion

In this paper, we address the stagnation of RoboCup com- petitions in the fields of contextual perception, real-time adaptation and flexible decision-making, mainly in regards to the Standard Platform League (SPL). We argue that our Situation-Aware FEar Learning (SAFEL) model has the necessary tools to leverage the SPL competition in these fields of research, by allowing robot players to learn the behaviour profile of the opponent team at runtime. Later, players can use this knowledge to predict when an undesirable outcome is imminent, thus having the chance to act towards preventing it. We discuss specific scenarios where SAFEL’s associative learning could help to increase the positive outcomes of a team during a soccer match by means of contextual adaptation

An Amygdala-Inspired Classical Conditioning Model Implemented on an FPGA for Home Service Robots

This study develops an intelligent system for home service robots mimicking human brain function that can manage common knowledge applicable to any environment and local knowledge reflecting its specific environment. Deep learning is effective for acquiring common knowledge because the performance of deep learning relies on the amounts of training and big training data that can be accessed for such knowledge; however, deep learning is ineffective for acquiring local knowledge because no big training data for such knowledge exist. Thus, we propose a brain-inspired learning model and system for acquiring local knowledge using small training data. We focus on the amygdala because its classical fear conditioning is effective for training using small training data. We propose an amygdala-inspired classical conditioning model comprising multiple self-organizing maps (lateral nucleus) and a fully connected neural network (central nucleus), imitating the function and structure of the amygdala. The proposed model is applied to a task of a waiter robot in a restaurant, and the model can learn customers\u27 preferences after only a few human-robot interactions. We accelerate the computation of the model and reduce its power consumption by proposing a hardware-oriented algorithm for the model and its digital hardware design and implement it in an XCZU9EG field programmable gate array. The hardware-oriented algorithm reduces the multiplication operations and exponential functions requiring huge hardware resources. The performance of the hardware operated at 150 MHz is 1,273 times faster than the software implementation on Arm Cortex-A53, and the power consumption of the chip is 5.009 W

Development of brain-inspired artificial intelligence model with functions of hippocampus, amygdala, and prefrontal cortex for embedded systems

九州工業大学博士学位論文 学位記番号：生工博甲第402号 学位授与年月日：令和3年3月25日第1章 はじめに|第2章 関連研究|第3章 個人の経験に基づいた知識の獲得|第4章 個人の経験に基づいた記憶の獲得及び記憶に基づいた予測|第5章 マルチタスク学習への適用|第6章 おわりに九州工業大学令和2年

Respect for patient autonomy in veterinary medicine: a relational approach

2017 Fall.Includes bibliographical references.This thesis considers the prospects for including respect for patient autonomy as a value in veterinary medical ethics. Chapter One considers why philosophers have traditionally denied autonomy to animals and why this is problematic; I also present contemporary accounts of animal ethics that recognize animals' capacity for and exercise of autonomy (or something similar, such as agency) as morally important. In Chapter Two, I review veterinary medical ethics today, finding that respect for patient autonomy is undiscussed or rejected outright as irrelevant. Extrapolating mainstream medical ethics' account of autonomy to veterinary medicine upholds this conclusion, as it would count all patients as "never-competent" and consider determining their autonomous choices impossible; thus welfare alone would be relevant. Chapter Three begins, in Part I, by describing the ways we routinely override patient autonomy in veterinary practice, both in terms of which interventions are selected and how care is delivered. I also show that some trends in the field suggest a nascent, implicit respect for patient autonomy. Part II of Chapter Three presents feminist criticisms of the mainstream approach to patient autonomy. I argue that the relational approach to autonomy advocated by such critics can be meaningfully applied in the veterinary realm. I advance an approach that conceives respect for patient autonomy in diachronic and dialogic terms, taking the patient as the foremost locus of respect. In Chapter Four, I turn to issues of practical implementation, such as interpreting what constitutes an animal's values and concerns, and assessing the effect of positive reinforcement training on autonomy. The Conclusion offers areas for future research while refuting the objection that a simpler, expanded welfare-based approach would yield the same substantive recommendations as my account

An anthropology of the police: semantic constructs of social order

The police play an increasing role in the public construction of order and control. This thesis explores the modes of thought by which police practices are generated in pursuit of this control. A publicly proclaimed approval of social research is not supported by the analysis and academic enquiry is shown to be a binary opposite to a preferred ‘practical mastery'. This suggests the police maintain structural invisibility while appearing to be massively accessible to society. The 'insider/anthropologist' operates in a kind of extended liminality, with the potential to illuminate such hidden beliefs by a seditious interpretation. Reflexive participant observation therefore threatens and creates anti-structural possibilities for a society obsessed with conserving known and inculcated practice. This analysis of manufactured reality reveals a dramatic creation of ‘real’ and marginal policemen and villains, where the use of extreme metaphor, language and masculine symbols of status translate thought into action. Intrusion of women into this ideal world creates structural anomaly, for the world of ‘crime’ is dramatised to reinforce traditional belief in a masculine criminal justice system. An exploration of ambiguity caused by policewomen illustrates their incorrect place in the world of 'street-visible crime control’. Archetypes of feminine susceptibility are invoked, just as the archetype of 'hero‘ is attributed to the detective, 'fighting his war against crime’. However, analysis explodes the mythology surrounding the idea of 'crime', showing it to be an arbitrary police construct directed against the 'dangerous classes', manipulated and produced as a social drama. The revelation that this major structuring principle is used to preserve a known social etiquette is impossible to acknowledge and explains how research or academic enquiry into philosophies of power must be resisted. The police world has a public face, but a well-concealed private reality which this semantic exploration makes apparent