Modeling Higher-Order Adaptive Evolutionary Processes by Multilevel Adaptive Agent Models

In this paper a fourth-order adaptive agent model based on a multilevel reified network model is introduced to describe different orders of adaptivity of the agent?s biological embodiment, as found in a case study on evolutionary processes. The adaptive agent model describes how the causal pathways for newly developed features affect the causal pathways of already existing features. This makes these new features one order of adaptivity higher than the existing ones. A network reification approach is shown to be an adequate means to model this

A Modeling Environment for Reified Temporal-Causal Networks: Modeling Plasticity and Metaplasticity in Cognitive Agent Models

Plasticity is a crucial adaptive characteristic of the brain. Relatively recently mechanisms have been found showing that plasticity itself is controlled by what is called metaplasticity. In this paper a modeling environment is introduced to develop and simulate reified temporal-causal network models that can be applied for cognitive agent models. It is shown how this environment is a useful tool to model plasticity combined with metaplasticity

An Adaptive Cognitive Agent Model for Development of a Hoarding Disorder and Recovery from it by Therapy

In this paper, an adaptive cognitive agent model is presented that describes both the process of development of a hoarding disorder and recovery from it by therapy. The adaptive agent model was evaluated by simulation experiments and comparison of them with expected patterns known from the literature. Moreover, mathematical analysis was performed of the equilibria of the agent model and used to verify the model. The model can be the basis for a virtual agent model that may support a therapist in their training or in their professional life

The Choice Between Bad and Worse: A Cognitive Agent Model for Desire Regulation under Stress

Desire for food intake often arises to get rid of negative emotions. On the other hand, negative emotion like anxiety, also brings along psychological health issues. In such a situation it’s quite a feasible option to get rid of the worse before the bad. In this paper a cognitive agent model for food desire regulation is presented wherein Hebbian learning helps in breaking the bond between anxiety or stress and desire for food intake as a result. Simulation results of the model illustrate food desire and its regulation

Learning to Explain Anger: An Adaptive Humanoid-Agent for Cyber-Aggression

Social media is one of the widely used channels for interpersonal communication, to express feelings and thoughts through certain feedback. Blogs or ecommerce websites share plenty of such information, which serves as a valuable asset, and is also used to make predictions. However, negative feedback can ruin the essence of such platforms, causing frustration among peers. This paper presents a computational network model of a humanoid agent for getting inappropriate feed-backs, who learns to react with a level of competence over aggression due to feedback. Tuning and evaluation of the model is done by performing simulation experiments based on public tweets and mathematical analysis respectively. This model can serve as an input to detect and handle aggression

From Good Intentions to Behaviour Change: Probabilistic Feature Diagrams for Behaviour Support Agents

Behaviour support technology assists people in organising their daily activities and changing their behaviour. A fundamental notion underlying such supportive technology is that of compliance with behavioural norms: do people indeed perform the desired behaviour? Existing technology employs a rigid implementation of compliance: a norm is either satisfied or not. In practice however, behaviour change norms are less strict: E.g., is a new norm to do sports at least three times a week complied with if it is occasionally only done twice a week? To address this, in this paper we formally specify probabilistic norms through a variant of feature diagrams, enabling a hierarchical decomposition of the desired behaviour and its execution frequencies. Further, we define a new notion of probabilistic norm compliance using a formal hypothesis testing framework. We show that probabilistic norm compliance can be used in a real-world setting by implementing and evaluating our semantics with respect to an existing daily behaviour dataset