Co-Evolutionary Learning for Cognitive Computer Generated Entities

In this paper, an approach is advocated to use a hybrid approach towards learning behaviour for computer generated entities (CGEs) in a serious gaming setting. Hereby, an agent equipped with cognitive model is used but this agent is enhanced with Machine Learning (ML) capabilities. This facilitates the agent to exhibit human like behaviour but avoid an expert having to define all parameters explicitly. More in particular, the ML approach utilizes co-evolution as a learning paradigm. An evaluation in the domain of one-versus-one air combat shows promising results

Interaction modeling based on human behavior classification

The variety of human behaviors makes user-product interaction difficult to manage and foresee, especially concerning users running into problems. This research considers several interaction problems and identifies recurring behaviors. Then, it highlights the users, products and environments' aspects corresponding to each of these behaviors. This makes possible foreseeing the behavior of specific users who run into problems while interacting with specific products in specific environments. The results are used to upgrade an existing user-product interaction modeling approach in order to make it able to suggest better-focused product improvements to designers as well as alternative problem solving to different users

Self-Organization in Collective Action: Elinor Ostrom’s Contributions and Complexity Theory

Elinor Ostrom’s contributions to the understanding self-organization in collective action processes are discussed from a complexity theory perspective. It is argued that complexity researchers can learn from Ostrom’s theory building process, as well her conceptualization of the conditions of self-organization in the management of common-pool resources. Her focus on self-organization helps rectify the problems with the assumption in the mainstream policy analysis that policy processes can be explained with external causes. The conceptual problems in her utility maximizing rational actor assumption and the potential for conceptual advancements in her recognition of complexity concepts are discussed. It is argued that Ostrom’s conceptual framework is sophisticated, but it lacks a dynamic understanding of the micro–macro relationships in complex governance systems, and that complexity theory offers the conceptual tools to remedy this problem

SOSIEL: a Cognitive, Multi-Agent, and Knowledge-Based Platform for Modeling Boundedly-Rational Decision-Making

Decision-related activities, such as bottom-up and top-down policy development, analysis, and planning, stand to benefit from the development and application of computer-based models that are capable of representing spatiotemporal social human behavior in local contexts. This is especially the case with our efforts to understand and search for ways to mitigate the context-specific effects of climate change, in which case such models need to include interacting social and ecological components. The development and application of such models has been significantly hindered by the challenges in designing artificial agents whose behavior is grounded in both empirical evidence and theory and in testing the ability of artificial agents to represent the behavior of real-world decision-makers. This dissertation advances our ability to develop such models by overcoming these challenges through the creation of: (a) three new frameworks, (b) two new methods, and (c) two new open-source modeling tools. The three new frameworks include: (a) the SOSIEL framework, which provides a theoretically-grounded blueprint for the development of a new generation of cognitive, multi-agent, and knowledge-based models that consist of agents empowered with cognitive architectures; (b) a new framework for analyzing the bounded rationality of decision-makers, which offers insight into and facilitates the analysis of the relationship between a decision situation and a decision-maker\u27s decision; and (c) a new framework for analyzing the doubly-bounded rationality (DBR) of artificial agents, which does the same for the relationship between a decision situation and an artificial agent\u27s decision. The two new methods include: (a) the SOSIEL method for acquiring and operationalizing decision-making knowledge, which advances our ability to acquire, process, and represent decision-making knowledge for cognitive, multi-agent, and knowledge-based models; and (b) the DBR method for testing the ability of artificial agents to represent human decision-making. The two open-source modeling tools include: (a) the SOSIEL platform, which is a cognitive, multi-agent, and knowledge-based platform for simulating human decision-making; and (b) an application of the platform as the SOSIEL Human Extension (SHE) to an existing forest-climate change model, called LANDIS-II, allowing for the analysis of co-evolutionary human-forest-climate interactions. To provide a context for examples and also guidelines for knowledge acquisition, the dissertation includes a case study of social-ecological interactions in an area of the Ukrainian Carpathians where LANDIS-II with SHE are currently being applied. As a result, this dissertation advances science by: (a) providing a theoretical foundation for and demonstrating the implementation of a next generation of models that are cognitive, multi-agent, and knowledge-based; and (b) providing a new perspective for understanding, analyzing, and testing the ability of artificial agents to represent human decision-making that is rooted in psychology