Stefan Kollmann

Examination of foundational AGI-agents in Artificial-Life simulations

The examination of AGI agents is an interdisciplinary challenge. This is particularly the case for their foundations, assumedly (in compliance with Damasio) drives and emotions. We demonstrate how these foundations of a human-like control system are tested using simplified exemplary cases as test-specifications. After showing how we model the required functions of the agents decision unit, we provide details of the incremental steps of our examination. As a first step, we use calibration-specifications, which we concretize with the help of psychoanalysts and neuroscientists and show how a flexible parameterization of the model generates expected behavior. This step enables a deeper model analysis in the second step of our examination, which corresponds to model exploration and provides further information for adaptions of the parameters, model, or assumptions. Overall, this examination methodology allows for a stepwise model development and examination and provides the ground for comparing the simulation data with empirical data, which we plan as a next step.

Emotion in Consumer Simulations for the Development and Testing of Recommendations for Marketing Strategies

To examine the impact factors and mechanisms of the decision to switch to green electricity, we develop a socio-cognitive agent-based simulation. Following seminal research in the field of decision making we focus on emotion and social norms as core mechanisms in consumer decisions. A survey of possible consumers provides the information how to calibrate the simulation, by which means a first validation is reached. Further data analysis supports model validation and exploration. Overall, this methodology provides the premises of using simulations for recommending marketing strategies that support the distribution of environmental-friendly energy providers.

Decision-making in the cognitive architecture SiMA

In a cognitive architecture, decision-making is the task that processes information from sensor data and stored knowledge to get appropriate action plans and actuator commands. Its aim is to make a decision in a given situation based upon available options and current goals of the system. In this paper, the decision-making process of the cognitive architecture SiMA is presented. Its unique features are the comprehensive evaluation of options, an application of case-based reasoning, as well as the management of resources by a two-step decision-making process. The implementation is verified through an artificial world implementation of a use case.

Usage of Cognitive Architectures in the Development of Industrial Applications - Utilization of a General Cognitive Process in the Domain Building Automation.

Cognitive architectures, which originate from the field of Artificial Intelligence, implement models for problem-solving and decision-making. These architectures have a wide room for implementation in industrial applications. The goal is to adapt a cognitive architecture to the demands of an application in the area of building automation. It is analyzed, why cognitive architectures are difficult to apply in industrial domain. The result of the analysis is a cognitive process, which is applied to an application in the building automation domain. The use of the architectures is demonstrated within a Java-based based middleware. There, the cognitive architecture is applied for the automatic generation and improvement of control strategies in building automation, which have the goal to minimize energy consumption with minimal reduction of the

Demo Abstract: Demonstration of new sensor and actuator equipment for distributed grids

Changes in the energy domain have created a high demand for new equipment and strategies to face its new challenges. To this end, stronger coordination between producers and consumers, as well as distributed control gain importance. This demonstration intends to show how developments from the project iniGrid can contribute towards this goal, by utilizing newly developed smart breakers to meet grid sided usage restrictions. The described demonstration system allows energy consumers more control over their usage and provides aggregators and energy suppliers as well as distribution system operators with additional means to improve grid stability and ways to counteract imminent catastrophic failures.

Local intelligence for a customer energy management system equipped with smart breakers

The electricity grid of the future needs to be smart to react to the currently changing production landscape caused by increasing generation of renewable energy at customer sites and the increased energy demands, driven by technological evolvement in consumer technology, e.g., heat pumps or electric vehicles. This paper tries to provide insights into first results of a work in progress creating a customer energy management system that utilizes the benefit of novel switchable breakers for customers and offers independent system operators, or distribution grid operators, some local intelligence which they can influence and use as predictable resources in their forecasts. These demand side management capabilities are only the first use cases under consideration.

Autonomous control strategy creation for building energy management

Building automation and control defines the energy efficiency of a building during its operation phase. Optimization of the underlying control strategies is still mainly done manually. By introducing a cognitive system and providing it with information on the building, its energy systems and the operation goals, this paper shows how to autonomously optimize control strategies for building energy systems. The system architecture consists of the cognitive system, an ontology and a physics simulator that allows to assess the quality of a control strategy. In this paper a first use case for optimization of a ventilation system is presented and the processes in the cognitive system that lead to the creation of control strategies are elaborated.