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This paper examines the evolution of agent strategies in a commons dilemma using a tag interaction model. Using a tag-mediated interaction model, individual's can determine their interactions based on their tag similarity. The experimental results show the significance of agent strategies that contribute to the overall value of the commons. The paper also examines the significance of viscosity on the emergence of cooperation in the commons dilemma. Viscosity represents a specific form of openness that will be examined in the paper. An initial series of experiments demonstrates the role of tags in the n-player dilemma while subsequent experiments examine the impact of openness on the evolution of agent strategies. The paper shows the emergence of cooperation through tag-mediated interactions. Simulation results show the emergence of strategies that contribute heavily to the value of the shared commons. The role of viscosity on the population strategy set is examined and this is similar to some forms of openness in multi-agent environments.

Self-organizing processes are crucial for the development of living beings. Practical applications in robots may benefit from the self-organization of behavior, e.g., to increase fault tolerance and enhance flexibility, provided that external goals can also be achieved. We present results on the guidance of self-organizing control by visual target stimuli and show a remarkable robustness to sensorimotor disruptions. In a proof of concept study an autonomous wheeled robot is learning an object finding and ball-pushing task from scratch within a few minutes in continuous domains. The robustness is demonstrated by the rapid recovery of the performance after severe changes of the sensor configuration.

We consider a market economy where two rational agents are able to learn the distribution of future events. In this context, we study whether moving away from the standard Bayesian belief updating, in the sense of under-reaction to some degree to new information, may be strategically convenient for traders. We show that, in equilibrium, strong under-reaction occurs, thus rational agents may strategically want to bias their learning process. Our analysis points out that the underlying mechanism driving ex-ante strategical decisions is diversity seeking. Finally, we show that, even if robust with respect to strategy selection, strong under-reaction can generate low realized welfare levels because of a long transient phase in which the agent makes poor predictions.

Policies designed to increase public engagement with biodiversity advocate increased education across a range of educational contexts. Evidence of the benefits of learning in natural environments (LINE) continues to be amassed. LINE affords direct benefits as diverse as educational, health and psychological and indirect benefits ranging from social to financial. Research into the value of LINE has failed to address the full range of benefits. Instead, there has been a narrow focus on easily measurable outcomes and a desire to seek answers to simplistic questions such as "does LINE raise standards more than learning in the classroom?" An attempt is made to outline the full range of benefits which are available to all school students. The outcomes include: benefits to individual participants (knowledge and understanding; skills; attitudes and behaviours; health and well-being; self-efficacy and self-worth); benefits to teachers, schools and the wider community, and benefits to the natural environment sector.

Several barriers exist to the effective delivery of LINE. These barriers can be grouped into those that challenge the natural environment sector and those that challenge schools. The challenges facing the sector include a lack of a coordinated effective approach to working with schools at a local level. The challenges facing schools include those frequently mentioned such as the risk of accidents, cost and curriculum pressures. However, another set of challenges exists, at local, institutional and personal levels. These challenges include teachers' confidence, self-efficacy and their access to training in using natural environments close to the school and further afield.

In distributed systems, traders mediate between clients and service providers. This paper introduces a trading model, which supports multiagent systems (MAS) and goes beyond simple trading in three ways: (a) Service composition — The trader composes complex services of the current service offers. During the composition, it checks the availability of the service offers. (b) Use of group agents — Group agents represent a group of agents with their individual policies and other context information. The trader can use the group agent's information for a pre-selection of service offers. (c) Adaptability — The trading model uses the notion of clients' trust into services and adapts to the clients' preferences and system policies. The trading model is used in a Computer-Supported Cooperative Work (CSCW) application, in which the trader finds adequate communication services for project teams with geographically distributed members.

A novel neural architecture for prediction in industrial control: the 'Double Recurrent Radial Basis Function network' (R²RBF) is introduced for dynamic monitoring and prognosis of industrial processes. Three applications of the R²RBF network on the prediction values confirmed that the proposed architecture minimizes the prediction error. The proposed R²RBF is excited by the recurrence of the output looped neurons on the input layer which produces a dynamic memory on both the input and output layers. Given the learning complexity of neural networks with the use of the back-propagation training method, a simple architecture is proposed consisting of two simple Recurrent Radial Basis Function networks (RRBF). Each RRBF only has the input layer with looped neurons using the sigmoid activation function. The output of the first RRBF also presents an additional input for the second RRBF. An unsupervised learning algorithm is proposed to determine the parameters of the Radial Basis Function (RBF) nodes. The K-means unsupervised learning algorithm used for the hidden layer is enhanced by the initialization of these input parameters by the output parameters of the RCE algorithm.

The integration of physiological functions in living organisms corresponds to the reconstruction of a biological system from its components. This calls for a sound theoretical framework based on the rigorous definition of the elementary physiological function within the context of multiple levels of biological organization. One of the main problems encountered in the neurosciences is that of extending the current theory of automata, as used in the study of artificial neural networks, to real neural networks. The difficulty arises because the theory of automata fails to take into account the various levels of biological organization involved in nervous activity. This article recalls the main elements of G. A. Chauvet's novel n-level field theory, i.e., the properties of non-symmetry and non-locality of functional interactions[8], and the S-propagator formalism that governs the propagation of a functional interaction across the different levels of the structural organization of a biological system[11]. The neural field equations derived from this theory allow the inclusion of multiple organizational levels of a biological system into the analysis by incorporating specific local models into a global non-local model. The main advantage of the method presented here is the simplification obtained by breaking down the physiological function into its components according to the time scales and space scales of operation. Moreover, the method takes into account the non-locality of the functional interaction, assuming it to be propagated at finite velocity in a continuous and hierarchical space. Finally, this approach allows the systematic study of physiological functions within a single theoretical framework, the complexity of which could be progressively increased by integrating specific local models as new findings become available.

Modifications of synaptic efficacy in the dentate gyrus were investigated during an olfactory associative task. A group of rats was trained to discriminate between a patterned electrical stimulation of the lateral olfactory tract, used as an artificial cue, associated with a water reward, and a natural odor associated with a flash of light. Monosynaptic field potential responses evoked by single electrical stimuli to the lateral perforant path were recorded in the granular layer of the ipsilateral dentate gyrus prior to and just after each training session. An early increase in this response was observed just after the first learning session but disappeared 24 hours later. Inversely, a synaptic depression developed across sessions, becoming significant at the onset of a last (fifth) session. When a group of naive animals was pseudo-conditioned, no increase was observed and the synaptic depression was noted since the onset of the second session.

In a group of rats similarly trained for only one session, and in which EPSPs were recorded throughout the 24 hours that followed, it was demonstrated that the increase lasted at least two hours, while the significant synaptic depression started after the fourth hour. These results are consistent with the early involvement of the dentate gyrus in learning the association between the cues and their respective rewards. These early integrative processes physiologically observed in dentate gyrus suggest early hippocampal processing before dentate gyrus reactivation via entorhinal cortex which will allow long-term memory storage in cortical areas once the meaning of the olfactory cues is learned.

SK channels are responsible for long-lasting hyperpolarization following action potential and contribute to the neuronal integration signal. This study evaluates the involvement of SK channels on learning and memory in rats, by comparing the effects of two SK channel blockers, i.e., apamin which recognizes SK2 and SK3 channels, and lei-Dab7 which binds SK2 channels only. lei-Dab7 totally competes and contests apamin binding on whole brain sections (IC₅₀: 11.4 nM). Using an olfactory associative task, intracerebroventricular blocker injections were tested on reference memory. Once the task was mastered with one odor pair, it was then tested with a new odor pair. Apamin (0.3 ng), injected before or after the acquisition session, improved new odor pair learning in a retention session 24 hours later, whereas lei-Dab7 (3 ng) did not significantly affect the mnesic processes. These results indicated that the blockage of SK channels by apamin facilitates consolidation on new odor associations; lei-Dab7, containing only SK2 subunits, remains without effect suggesting an involvement of SK3 channels in the modulation of the mnesic processes.

In the present work, it was experimentally shown that a neuron in vitro was capable of responding in a manner similar to habituation, Pavlov's reflex and avoidance of the reinforcements. The locality of plastic property modifications and molecular morphology, as well as the connection between functional activity and cytoskeleton have been revealed. A hypothesis is formulated that the neuron is a molecular system which may exercise the control, forecast, recognition, and classification. The basic principles of the molecular mechanisms of the responses underlying integrative activity, learning and memory at the neuronal level are discussed.

Training experimental animals to discriminate between different visual stimuli has been an important tool in cognitive neuroscience as well as in vision research for many decades. Current methods used for visual choice discrimination training of zebrafish require human observers for response tracking, stimulus presentation and reward delivery and, consequently, are very labor intensive and possibly experimenter biased. By combining video tracking of fish positions, stimulus presentation on computer monitors and food delivery by computer-controlled electromagnetic valves, we developed a method that allows for a fully automated training of multiple adult zebrafish to arbitrary visual stimuli in parallel. The standardized training procedure facilitates the comparison of results across different experiments and laboratories and contributes to the usability of zebrafish as vertebrate model organisms in behavioral brain research and vision research.

In this work we present a new mechatronic platform for measuring behavior of nonhuman primates, allowing high reprogrammability and providing several possibilities of interactions. The platform is the result of a multidisciplinary design process, which has involved bio-engineers, developmental neuroscientists, primatologists, and roboticians to identify its main requirements and specifications. Although such a platform has been designed for the behavioral analysis of capuchin monkeys (Cebus apella), it can be used for behavioral studies on other nonhuman primates and children. First, a state-of-the-art principal approach used in nonhuman primate behavioral studies is reported. Second, the main advantages of the mechatronic approach are presented. In this section, the platform is described in all its parts and the possibility to use it for studies on learning mechanism based on intrinsic motivation discussed. Third, a pilot study on capuchin monkeys is provided and preliminary data are presented and discussed.

Consciousness is a topic of considerable human curiosity with a long history of philosophical analysis and debate. We consider there is nothing particularly complicated about consciousness when viewed as a necessary process of the vertebrate nervous system. Here, we propose a physiological "explanatory gap" is created during each present moment by the temporal requirements of neuronal activity. The gap extends from the time exteroceptive and proprioceptive stimuli activate the nervous system until they emerge into consciousness. During this "moment", it is impossible for an organism to have any conscious knowledge of the ongoing evolution of its environment. In our schematic model, a mechanism of "afference copy" is employed to bridge the explanatory gap with consciously experienced percepts. These percepts are fabricated from the conjunction of the cumulative memory of previous relevant experience and the given stimuli. They are structured to provide the best possible prediction of the expected content of subjective conscious experience likely to occur during the period of the gap. The model is based on the proposition that the neural circuitry necessary to support consciousness is a product of sub/preconscious reflexive learning and recall processes. Based on a review of various psychological and neurophysiological findings, we develop a framework which contextualizes the model and briefly discuss further implications.

The expression "knowledge is power" has become a convenient soundbyte for all and sundry. Boardrooms and corporate luncheons are abuzz with sweeping remarks such as "we need to effectively manage our knowledge" or "harnessing the power of knowledge is the way forward." Despite an abundance of literature on the subject of knowledge management, many firms struggle to manage their knowledge assets. Of all contemporary management techniques, knowledge management demonstrates the biggest gap between promise and results realised. Why do some organisations benefit from the implementation of KM systems while other organisations do not? Can this intangible entity called "knowledge" be managed and harnessed at all, or is it merely a matter of throwing the dice and hoping for the best? Can the rhetoric be translated into a reality? This paper presents a thorough literature review followed by a case study approach. The authors have delved deep into the realities of an educational institute as a study of an organisation. Some propositions that can be helpful in translating the knowledge management "rhetoric" into a "practical reality" have been proposed. The authors have attempted to identify the causes of failure/success of any KM initiative.

This paper aims to identify two different knowledge management (KM) systems and their underlying capabilities by accounting for two contextual factors: organisational structures and type of knowledge. Specifically, it seeks to explore how two different organisational structures (mechanistic and organic) shape the way explicit and tacit knowledge is shared, created, and learned. The paper uses a case-based approach of two sports teams as archetypal contexts to inform management research. Findings suggest that a mechanistic structure (American football) emphasises explicit knowledge for sharing of specific directives, centralised, incremental knowledge creation, and organisational learning through memorisation and repetitious actions. In an organic structure (ice hockey), sharing of tacit knowledge, decentralised novel knowledge creation, and organisational learning through empowered experiential learning episodes are emphasised. Findings illustrate the importance of accounting for organisational structures and knowledge needed for different KM systems geared towards efficiency and routine work, and flexibility and non-routine work.

This research aims to investigate the effects of social media use on higher education teaching and learning as well as the students’ academic performance. A total of 275 students and faculty members from the College of Computer Science and Information Technology at Imam Abdulrahman Bin Faisal University took part in the study. The participants answered survey questions to analyse information on their use of social media in education and how that has affected their teaching, learning and grades. A majority of the participants reported that they used social media in training. However, they also stated that social media platforms were beneficial in academic matters. The number of participants who stated that the use of social media in learning helped improve their grades was 43%. The other 57% thought that social media had no impact on their grades or had an adverse effect or were undecided.

Learning organisations facilitate the acquisition of new knowledge and skills for both organisations and their members, enabling the application of the latest insights in a dynamic environment. This study addresses a research gap by constructing a model identifying the factors influencing learning organisations and their subsequent impact. The research delves into uncovering the catalysts behind learning organisations concerning the career advancement of information technology (IT) employees and their intent to remain in their roles. The study, conducted in a developing country, India, employs an explanatory research design to explore the interrelationship between learning organisations, career progression and employee retention. Primary and secondary data have been used for this study. The primary data has been collected from 389 IT sector employees at various employment positions in Chennai through a structured and standard questionnaire based on the Dimensions of Learning Organizations Questionnaire (DLOQ) by Watkins, KE and Marsick, VJ (2023) [Rethinking workplace learning and development catalyzed by complexity. Human Resource Development Review, 22(3), 333-344. doi:10.1177/15344843231186629], demonstrating a high reliability at 96.4%.

In this paper, the single machine total weighted completion time scheduling problem is studied. The jobs have nonzero release time and processing time increases during the production due to the effect of deterioration on the machine. An operator sets up the machine and manually loads the job in the machine and unloads it at the end of the working time. The setup time and the removal time are influenced by the ability of the worker due to his work experience and learning capacity. Heuristic algorithms are proposed to solve the scheduling problem, and their efficiency is evaluated on a wide benchmark.

Trustworthy and reliable applications built using intelligent software agents aim to provide improved performance using its characteristics. Agents introduced in various architectures represent its functionality as functional elements of the architecture and shows the interaction between other components present in the architecture. The Internet of things (IoT) reveals as a frequent technology that allows accessing the physical objects present in the world. IoT systems utilize wireless sensor network to transmit and receive data by establishing communication. Wireless Sensor Networks transmits digital signals to the cyber-world for analyzing and processing the information into useful data by either formulating or communicating with the intelligent and innovative system. While talking about IoT and WSN, agents introduced in such environments assist in making decisions quickly by perceiving the input from the environment. The number of agents needed for an application depends upon the complexity of the problem. Multi-Agent architectures discussed in the article describe their association, roles, functionality and interaction. This paper gives a detailed survey of various agent/multi-agent learning architectures introduced over IoT and WSN. Moreover, this survey with the performance and the SWOT analysis on the Agent-based learning architecture helps the reader and paves a way to pursue research on Agent-based architectural deployment over IoT and WSN paradigms.

This paper gives an overview on current and forthcoming research activities of the Collaborative Research Center 588 "Humanoid Robots — Learning and Cooperating Multimodal Robots" which is located in Karlsruhe, Germany. Its research activities can be divided into the following areas: mechatronic robot system components like lightweight 7 DOF arms, 5-fingered dexterous hands, an active sensor head and a spine type central body and skills of the humanoid robot system; multimodal man-machine interfaces; augmented reality for modeling and simulation of robots, environment and user; and finally, cognitive abilities. Some of the research activities are described in this paper, and we introduce the application scenario testing the robot system. In particular, we present a robot teaching center and the execution which is of type "household."