A Network-Oriented Adaptive Agent Model for Learning Regulation of a Highly Sensitive Person’s Response

In recent years, the topic of emotion reading has increasingly received attention from researchers in Cognitive Science and Artificial Intelligence. To study this phenomenon, in this paper an adaptive agent model is presented with capabilities to interpret another agent’s emotions. The presented agent model is based on recent advances in neurological context. First a non-adaptive agent model for emotion reading is described involving (preparatory) mirroring body states of the other agent. Here emotion reading is modelled taking into account the Simulation Theory perspective as known from the literature, involving the own body states and emotions in reading somebody else’s emotions. This models an agent that first develops the same feeling, and after feeling the emotion imputes it to the other agent. Next the agent model is extended to an adaptive model based on a Hebbian learning principle to develop a direct connection between a sensed stimulus concerning another agent’s body state (e.g., face expression) and the emotion recognition state. In this adaptive agent model the emotion is imputed to the other agent before it is actually felt. The agent model has been designed based on principles of neural modelling, and as such has a close relation to a neurological realisation.Keywordsagent modelemotion readingcognitivetheory of mindadaptive

In this paper an adaptive agent model is presented that models how recurrent dreams such as nightmares occur. In the agent model recurring dreams emerge as scripts of connected dream episodes in which the connections between the episodes strengthen over time by Hebbian learning. The model was evaluated by a number of simulation experiments.

Conversational agents have been widely adopted in dialogue systems for various business purposes. Many existing conversational agents are rule-based and require significant human intervention to adapt the knowledge and conversational flow. In this paper, we propose a graph-based adaptive conversational agent model which is capable of learning knowledge from human beings and adapting the knowledge-base according to human-agent interactions. Studies to evaluate the proposed model are conducted and presented, which compare the responses from the proposed adaptive agent model and a conventional agent.

This paper describes an autonomous adaptive agent model of the UK market in electricity, where the agents represent electricity generating companies. We briefly describe the UK market in electricity generation, then detail the simplifications we have made. Our current model consists of a single adaptive agent bidding against several non-adaptive agents. The adaptive agent uses a hierarchical agent structure with two Learning Classifier Systems to evolve market bidding rules to meet two objectives. We detail how the agent interacts with its environment, the particular problems this environment presents to the agent and the agent and classifier architectures we used in our experiments. We present the results and conclude that using our structure can improve performance.

Maintenance accounts for a major cost of software development. The design of software systems from the very beginning phase to achieve adaptive behaviour should, therefore, be recognised of its importance in practice. Adaptive Agent Model (AAM) is aimed towards this end via using Agent-oriented Model-Driven Architecture. Models are derived from requirements and reconfigured continuously by domain experts to reflect changing business needs. Agents interpret these models, at runtime, to choose alternative partners in collaboration or reusable components in computation, in an adaptive manner. Adopting an event-driven architecture at runtime, AAM has its agents and knowledge-based control readily applicable to robots and their on-board control extension. The paper describes work towards this goal and ultimately, this will demonstrate the feasibility of AAM in software adaptivity via the robot platform, and also help to achieve robot adaptivity through corresponding AAM support.

BackgroundIndividuals with high sensory processing sensitivity (SPS) (“highly sensitive persons”) are thought to be easily excitable and overwhelmed, highly attentive to aesthetic impressions, and particularly sensitive to sensory stimulation. Public discourse suggests that those who describe themselves as highly sensitive see themselves as fundamentally different from others, and view their personality as a gift and a burden. From a clinical personality perspective, high sensitivity could be considered to have substantial overlaps with hypersensitive narcissism, or generally vulnerable narcissism.MethodWe investigated the associations and shared nomological networks between high sensitivity and hypersensitive narcissism in two studies using convenience and representative samples (n1 = 280, n2 = 310).ResultsThere is evidence for replicable associations between SPS and hypersensitive (.53 ≤ r ≤ .54) as well as vulnerable narcissism (.44 ≤ r ≤ .54), associations were not attributable to general neuroticism. Nomological networks were similar and pointed to a neurotic‐introverted personality profile with reduced personality functioning. Latent class analyses further pointed to substantial and practically relevant person‐level covariance.ConclusionSensory processing sensitivity and hypersensitive narcissism are substantially related constructs. For clinicians, this points to the importance of being attentive to narcissistic self‐regulatory strategies in individuals presenting as highly sensitive.

A special issue on ‘the emergence of language: neural and adaptive agent models’

Chapter 5 - Etiology of sensory processing sensitivity: Neurobiology, genes, and evolution

Parents play an important role in the mental development of a child. In our previous work, we addressed how a narcissistic parent influences a child (online/offline) when (s)he is happy and admires the child. Now, we address the influence of a parent who is not so much pleased, and may curse the child for being the reason for his or her unhappiness. An abusive relationship with a parent can also cause trauma and poor mental health of the child. We also address how certain coping behaviors can help the child cope with such a situation. Therefore, the aim of the study is threefold. We present an adaptive agent model of a child, while incorporating the concept of mirroring through social contagion, the avoidance behaviors from a child, and the effects of regulation strategies to cope with stressful situations.

We investigate an evolution model of adaptive self-learning agents. The control system of agents is based on a neural network adaptive critic design. Each agent is a broker that predicts stock price changes and uses its predictions for action selection. We analyzed different regimes of learning and evolution and demonstrated that 1) evolution and learning together are more effective in searching for the optimal agent policy than evolution alone or learning alone; 2) in some regimes the Baldwin effect (genetic assimilation of initially acquired adaptive learning features during the course of Darwinian evolution) is observed; 3) inertial switching between two behavioral tactics similar to searching adaptive behavior of simple animals takes place during initial stages of evolutionary processes.KeywordsStock PriceAgent ResourceAdaptive AgentBaldwin EffectGenetic AssimilationThese 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.

Interpersonal sensitivity is an aspect of Sensory Processing Sensitivity (SPS) that has been unexplored precisely despite potentially playing an even more significant role in individuals with SPS. The results of various studies on individuals with SPS suggest that this trait is accompanied by a high interpersonal sensitivity, which refers to an increased sensitivity to the emotional states of individuals with whom one interacts; however, no measurement instrument directly evaluates it. This research aimed to develop an instrument to assess high interpersonal sensitivity and analyze its psychometric properties. Four hundred twenty-nine university students aged 18 to 29 participated. Confirmatory Factor Analysis supported a three-factor structure (awareness of subtleties, overstimulation, and persistent effect) consistent with Exploratory Factor Analysis findings. The internal consistency values for the total scale and the three factors were adequate, and the validity evidence was congruent with the construct. Despite needing further studies, the High Interpersonal Sensitivity Scale (HISS) shows adequate psychometric properties for measuring high interpersonal sensitivity in adults.

Sensory-processing sensitivity differentiates individuals according to responsivity to internal and external stimuli. It has been positively correlated with depressive symptoms. Meanwhile, sense of coherence, an individual’s perception that stressors are comprehensible, manageable, and meaningful for their life, could improve depression. This cross-sectional study investigated the moderation effect of sense of coherence on the relationship between sensory-processing sensitivity and depressive symptoms in university students. Japanese students (N = 430) participated in a questionnaire survey that assessed levels of sensory-processing sensitivity, sense of coherence, and depressive symptoms. The results showed that a strong sense of coherence moderated the relationship between sensory-processing sensitivity and depressive symptoms in university students.

Sensory-Processing Sensitivity (SPS) characterizes people’s characteristic sensitivity to experience. SPS is a young field of research. There are limitations in the study of SPS and religiosity and spirituality. This study seeks to explore how Muslims with high SPS view the loss they have experienced and how they cope with the loss from an Islamic perspective. Interpretative Phenomenological Analysis (IPA) was used as the method of the study to allow for an in-depth exploration of how participants make sense of their loss experiences. The study recruited five high SPS Malaysian Muslims to explore the loss and coping experiences they had during the COVID-19 crisis and how they viewed the experiences in Islamic perspectives. The study found three themes in the application of Islamic perspectives in viewing the loss experienced by the participants and the way they coped with the loss. The Malaysian Muslim participants applied Islamic teachings as protective and therapeutic aspects to cope with the loss of loved ones, which is consistent with previous studies on Islamic religiosity. It is recommended that future SPS studies focus on exploring how individuals with different levels of SPS view religiosity and spirituality aspects of life events, especially within the Malaysian Muslim community.

Currently, there is a lack of tools to build autonomous software systems in a pervasive and distributed context. In this paper, we present a model and a development tool called Agentphi for building flexible agent architectures from fine-grained components. Reusable components implement non-functional mechanisms such as communication, mobility or adaptation skills. Each agent can dynamically and autonomously change its components to suit its runtime context, improving safety and performances of applications, particularly in open, pervasive or large-scale distributed applications. We also show how to use our agents to build applications from autonomous and adaptable systems.

Simulation-based models are becoming a promising research tool in financial markets. A general Complex Adaptive System can be tailored to different application scenarios. This paper describes an application of a Complex Adaptive System-based agent model in stock trades signalling. The model has been evaluated using historical movement of Bank of America stock. Agents in the system are initialized using random decision rules. Genetic algorithms and machine learning methods are utilized to reduce the sample space and improve the decision rules. Final rules are generated via Monte Carlo simulation and modified with a market momentum estimate. By following the advice suggested by the model. The hypothetical investors have outperformed the S&P 500 index and buy-and-hold investors. Compared with benchmark agents with buy-and-hold strategy on stock and index respectively, the model achieved higher return even in periods of stock’s poor performance. The stock trade-signalling model is implemented using the Netlogo framework.