A flexible component-based robot control architecture for hormonal modulation of behaviour and affect

This document is the Accepted Manuscritpt of a paper published in Proceedings of 18th Annual Conference, TAROS 2017, Guildford, UK, July 19–21, 2017. Under embargo. Embargo end date: 20 July 2018. The final publication is available at Springer via https://link.springer.com/chapter/10.1007%2F978-3-319-64107-2_36. © 2017 Springer, Cham.In this paper we present the foundations of an architecture that will support the wider context of our work, which is to explore the link between affect, perception and behaviour from an embodied perspective and assess their relevance to Human Robot Interaction (HRI). Our approach builds upon existing affect-based architectures by combining artificial hormones with discrete abstract components that are designed with the explicit consideration of influencing, and being receptive to, the wider affective state of the robot

Distributed Cognition as the Basis for Adaptation and Homeostasis in Robots

Many researchers approach the problem of building autonomous systems by looking to biology for inspiration. This has given rise to a wide-range of artificial systems mimicking their biological counterparts—artificial neural networks, artificial endocrine systems, and artificial musculoskeletal systems are prime examples. While these systems are succinct and work well in isolation, they can become cumbersome and complicated when combined to perform more complex tasks. Autonomous behaviour is one such complex task. This thesis considers autonomy as the complex behaviour it is, and proposes a bottom-up approach to developing autonomous behaviour from cognition. This consists of investigating how cognition can provide new approaches to the current limitations of swarm systems, and using this as the basis for one type of autonomous behaviour: artificial homeostasis. Distributed cognition, a form of emergent cognition, is most often described in terms of the immune system and social insects. By taking inspiration from distributed cognition, this thesis details the development of novel algorithms for cognitive decision-making and emergent identity in leaderless, homogenous swarms. Artificial homeostasis is provided to a robot through an architecture that combines the cognitive decision-making algorithm with a simple associative memory. This architecture is used to demonstrate how a simple architecture can endow a robot with the capacity to adapt to an unseen environment, and use that information to proactively seek out what it needs from the environment in order to maintain its internal state

Hedonic Quality or Reward? A Study of Basic Pleasure in Homeostasis and Decision Making of a Motivated Autonomous Robot

© The Author (s) 2016. Published by SAGE. This is an Open Access article distributed under the terms of the Creative Commons Attribution 3.0 License (http://www.creativecommons.org/licenses/by/3.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).We present a robot architecture and experiments to investigate some of the roles that pleasure plays in the decision making (action selection) process of an autonomous robot that must survive in its environment. We have conducted three sets of experiments to assess the effect of different types of pleasure---related versus unrelated to the satisfaction of physiological needs---under different environmental circumstances. Our results indicate that pleasure, including pleasure unrelated to need satisfaction, has value for homeostatic management in terms of improved viability and increased flexibility in adaptive behavior.Peer reviewedFinal Published versio

Bioinspired approaches for coordination and behaviour adaptation of aerial robot swarms

Behavioural adaptation is a pervasive component in a myriad of animal societies. A well-known strategy, known as Levy Walk, has been commonly linked to such adaptation in foraging animals, where the motion of individuals couples periods of localized search and long straight forward motions. Despite the vast number of studies on Levy Walks in computational ecology, it was only in the past decade that the first studies applied this concept to robotics tasks. Therefore, this Thesis draws inspiration from the Levy Walk behaviour, and its recent applications to robotics, to design biologically inspired models for two swarm robotics tasks, aiming at increasing the performance with respect to the state of the art. The first task is cooperative surveillance, where the aim is to deploy a swarm so that at any point in time regions of the domain are observed by multiple robots simultaneously. One of the contributions of this Thesis, is the Levy Swarm Algorithm that augments the concept of Levy Walk to include the Reynolds’ flocking rules and achieve both exploration and coordination in a swarm of unmanned aerial vehicles. The second task is adaptive foraging in environments of clustered rewards. In such environments behavioural adaptation is of paramount importance to modulate the transition between exploitation and exploration. Nature enables these adaptive changes by coupling the behaviour to the fluctuation of hormones that are mostly regulated by the endocrine system. This Thesis draws further inspiration from Nature and proposes a second model, the Endocrine Levy Walk, that employs an Artificial Endocrine System as a modulating mechanism of Levy Walk behaviour. The Endocrine Levy Walk is compared with the Yuragi model (Nurzaman et al., 2010), in both simulated and physical experiments where it shows its increased performance in terms of search efficiency, energy efficiency and number of rewards found. The Endocrine Levy Walk is then augmented to consider social interactions between members of the swarm by mimicking the behaviour of fireflies, where individuals attract others when finding suitable environmental conditions. This extended model, the Endocrine Levy Firefly, is compared to the Levy+ model (Sutantyo et al., 2013) and the Adaptive Collective Levy Walk Nauta et al. (2020). This comparison is also made both in simulated and physical experiments and assessed in terms of search efficiency, number of rewards found and cluster search efficiency, strengthening the argument in favour of the Endocrine Levy Firefly as a promising approach to tackle collaborative foragin

Homeostatic action selection for simultaneous multi-tasking

Mobile robots are rapidly developing and gaining in competence, but the potential of available hardware still far outstrips our ability to harness. Domain-specific applications are most successful due to customised programming tailored to a narrow area of application. Resulting systems lack extensibility and autonomy, leading to increased cost of development. This thesis investigates the possibility of designing and implementing a general framework capable of simultaneously coordinating multiple tasks that can be added or removed in a plug and play manner. A homeostatic mechanism is proposed for resolving the contentions inevitably arising between tasks competing for the use of the same robot actuators. In order to evaluate the developed system, demonstrator tasks are constructed to reach a goal location, prevent collision, follow a contour around obstacles and balance a ball within a spherical bowl atop the robot. Experiments show preliminary success with the homeostatic coordination mechanism but a restriction to local search causes issues that preclude conclusive evaluation. Future work identifies avenues for further research and suggests switching to a planner with the sufficient foresight to continue evaluation."This work was supported by the Engineering and Physical Sciences Research Council [grant number EP/K503162/1]." -- Acknowledgement

Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

© 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care

Ecological adaptation in the context of an actor-critic

Biological beings are the result of an evolutionary and developmental process of adaptation to the environment they perceive and where they act. Animals and plants have successfully adapted to a large variety of environments, which supports the ideal of inspiring artificial agents after biology and ethology. This idea has been already suggested by previous studies and is extended throughout this thesis. However, the role of perception in the process of adaptation and its integration in an agent capable of acting for survival is not clear.Robotic architectures in AI proposed throughout the last decade have broadly addressed the problems of behaviour selection, namely deciding "what to do next", and of learning as the two main adaptive processes. Behaviour selection has been commonly related to theories of motivation, and learning has been bound to theories of reinforcement. However, the formulation of a general theory including both processes as particular cases of the same phenomenon is still an incomplete task. This thesis focuses again on behaviour selection and learning; however it proposes to integrate both processes by stressing the ecological relationship between the agent and its environment. If the selection of behaviour is an expression of the agent's motivations, the feedback of the environment due to behaviour execution can be viewed as part of the same process, since it also influences the agent's internal motivations and the learning processes via reinforcement. I relate this to an argument supporting the existence of a common neural substrate to compute motivation and reward, and therefore relating the elicitation of a behaviour to the perception of reward resulting from its executionAs in previous studies, behaviour selection is viewed as a competition among parallel pathways to gain control over the agent's actuators. Unlike for the previous cases, the computation of every motivation in this thesis is not anymore the result of an additive or multiplicative formula combining inner and outer stimuli. Instead, the ecological principle is proposed to constrain the combination of stimuli in a novel fashion that leads to adaptive behavioural patterns. This method aims at overcoming the intrinsic limitations of any formula, the use of which results in behavioural responses restricted to a set of specific patterns, and therefore to the set of ethological cases they can justify. External stimuli and internal physiology in the model introduced in this thesis are not combined a priori. Instead, these are viewed from the perspective of the agent as modulatory elements biasing the selection of one behaviour over another guided by the reward provided by the environment, being the selection performed by an actor-critic reinforcement learning algorithm aiming at the maximum cumulative reward.In this context, the agent's drives are the expression of the deficit or excess of internal resources and the reference of the agent to define its relationship with the environment. The schema to learn object affordances is integrated in an actor-critic reinforcement learning algorithm, which is the core of a motivation and reinforcement framework driving behaviour selection and learning. Its working principle is based on the capacity of perceiving changes in the environment via internal hormonal responses and of modifying the agent's behavioural patterns accordingly. To this end, the concept of reward is defined in the framework of the agent's internal physiology and is related to the condition of physiological stability introduced by Ashby, and supported by Dawkins and Meyer as a requirement for survival. In this light, the definition of the reward used for learning is defined in the physiological state, where the effect of interacting with the environment can be quantified in an ethologically consistent manner.The above ideas on motivation, behaviour selection, learning and perception have been made explicit in an architecture integrated in an simulated robotic platform. To demonstrate the reach of their validity, extensive simulation has been performed to address the affordance learning paradigm and the adaptation offered by the framework of the actor-critic. To this end, three different metrics have been proposed to measure the effect of external and internal perception on the learning and behaviour selection processes: the performance in terms of flexibility of adaptation, the physiological stability and the cycles of behaviour execution at every situation. In addition to this, the thesis has begun to frame the integration of behaviours of an appetitive and consummatory nature in a single schema. Finally, it also contributes to the arguments disambiguating the role of dopamine as a neurotransmitter in the Basal Ganglia

Pemahaman pelajar tingkatan lima katering terhadap bab kaedah memasak dalam mata pelajaran teknologi katering

Bab Kaedah Memasak merupakan salah satu bab yang penting dalam mata pelajaran Teknologi Katering. Faktor terpenting adalah memastikan pelajar menguasai serta memahami konsepnya adalah melalui proses pengajaran dan pembelajaran yang betul. Tinjauan awal di Sekolah Menengah Teknik yang menawarkan Kursus Katering, menunjukkan bahawa kebanyakan pelajar sukar untuk menguasai dan memahami bab tersebut. Berdasarkan hasil tinjauan , pengkaji ingin mengenalpasti pemasalahan dalam memahami bab tersebut. Di samping itu juga, pengkaji ingin mengenalpasti adakah pencapaian pelajar dalam PMR, minat, motivasi dan gaya pembelajaran mempengaruhi pemahaman pelajar, Kajian rintis telah dilakukan terhadap 10 orang responden dengan nilai alpha 0.91. Ini menunjukkan kebolehpercayaan terhadap kajian di jalankan adalah tinggi. Responden adalah terdiri daripada 30 orang pelajar Tingkatan Lima (ERT) Sekolah Menengah Teknik Muar, Johor. Keputusan skor min keseluruhan menunjukkan pelajar berminat dan mempunyai motivasi ynag baik dalam bidang ini. Namun begitu, gaya pembelajaran yang diamalkan tidak sesuai dan antara pemyebab wujudnya pemasalahan dalam memahami bab Kaedah Memasak. Ujian kolerasi menunjukkan bahawa tidak terdapat sebarang hubungan signifikan antara pencapaian PMR pelajar dengan pemahaman bab tersebut. Sementara minat, motivasi dan gaya pembelajaran membuktikan ada hubungan signifikan dengan pemahaman pelajar dalam bab Kaedah Memasak