CernoCAMAL : a probabilistic computational cognitive architecture

This thesis presents one possible way to develop a computational cognitive architecture, dubbed CernoCAMAL, that can be used to govern artificial minds probabilistically. The primary aim of the CernoCAMAL research project is to investigate how its predecessor architecture CAMAL can be extended to reason probabilistically about domain model objects through perception, and how the probability formalism can be integrated into its BDI (Belief-Desire-Intention) model to coalesce a number of mechanisms and processes. The motivation and impetus for extending CAMAL and developing CernoCAMAL is the considerable evidence that probabilistic thinking and reasoning is linked to cognitive development and plays a role in cognitive functions, such as decision making and learning. This leads us to believe that a probabilistic reasoning capability is an essential part of human intelligence. Thus, it should be a vital part of any system that attempts to emulate human intelligence computationally. The extensions and augmentations to CAMAL, which are the main contributions of the CernoCAMAL research project, are as follows: - The integration of the EBS (Extended Belief Structure) that associates a probability value with every belief statement, in order to represent the degrees of belief numerically. - The inclusion of the CPR (CernoCAMAL Probabilistic Reasoner) that reasons probabilistically over the goal- and task-oriented perceptual feedback generated by reactive sub-systems. - The compatibility of the probabilistic BDI model with the affect and motivational models and affective and motivational valences used throughout CernoCAMAL. A succession of experiments in simulation and robotic testbeds is carried out to demonstrate improvements and increased efficacy in CernoCAMAL’s overall cognitive performance. A discussion and critical appraisal of the experimental results, together with a summary, a number of potential future research directions, and some closing remarks conclude the thesis

CernoCAMAL : a probabilistic computational cognitive architecture

This thesis presents one possible way to develop a computational cognitive architecture, dubbed CernoCAMAL, that can be used to govern artificial minds probabilistically. The primary aim of the CernoCAMAL research project is to investigate how its predecessor architecture CAMAL can be extended to reason probabilistically about domain model objects through perception, and how the probability formalism can be integrated into its BDI (Belief-Desire-Intention) model to coalesce a number of mechanisms and processes.The motivation and impetus for extending CAMAL and developing CernoCAMAL is the considerable evidence that probabilistic thinking and reasoning is linked to cognitive development and plays a role in cognitive functions, such as decision making and learning. This leads us to believe that a probabilistic reasoning capability is an essential part of human intelligence. Thus, it should be a vital part of any system that attempts to emulate human intelligence computationally.The extensions and augmentations to CAMAL, which are the main contributions of the CernoCAMAL research project, are as follows:- The integration of the EBS (Extended Belief Structure) that associates a probability value with every belief statement, in order to represent the degrees of belief numerically.- The inclusion of the CPR (CernoCAMAL Probabilistic Reasoner) that reasons probabilistically over the goal- and task-oriented perceptual feedback generated by reactive sub-systems.- The compatibility of the probabilistic BDI model with the affect and motivational models and affective and motivational valences used throughout CernoCAMAL.A succession of experiments in simulation and robotic testbeds is carried out to demonstrate improvements and increased efficacy in CernoCAMAL’s overall cognitive performance. A discussion and critical appraisal of the experimental results, together with a summary, a number of potential future research directions, and some closing remarks conclude the thesis

Computational Natural Philosophy: A Thread from Presocratics through Turing to ChatGPT

Modern computational natural philosophy conceptualizes the universe in terms of information and computation, establishing a framework for the study of cognition and intelligence. Despite some critiques, this computational perspective has significantly influenced our understanding of the natural world, leading to the development of AI systems like ChatGPT based on deep neural networks. Advancements in this domain have been facilitated by interdisciplinary research, integrating knowledge from multiple fields to simulate complex systems. Large Language Models (LLMs), such as ChatGPT, represent this approach's capabilities, utilizing reinforcement learning with human feedback (RLHF). Current research initiatives aim to integrate neural networks with symbolic computing, introducing a new generation of hybrid computational models.Comment: 17 page

A society of mind approach to cognition and metacognition in a cognitive architecture

This thesis investigates the concept of mind as a control system using the "Society of Agents" metaphor. "Society of Agents" describes collective behaviours of simple and intelligent agents. "Society of Mind" is more than a collection of task-oriented and deliberative agents; it is a powerful concept for mind research and can benefit from the use of metacognition. The aim is to develop a self configurable computational model using the concept of metacognition. A six tiered SMCA (Society of Mind Cognitive Architecture) control model is designed that relies on a society of agents operating using metrics associated with the principles of artificial economics in animal cognition. This research investigates the concept of metacognition as a powerful catalyst for control, unify and self-reflection. Metacognition is used on BDI models with respect to planning, reasoning, decision making, self reflection, problem solving, learning and the general process of cognition to improve performance.One perspective on how to develop metacognition in a SMCA model is based on the differentiation between metacognitive strategies and metacomponents or metacognitive aids. Metacognitive strategies denote activities such as metacomphrension (remedial action) and metamanagement (self management) and schema training (meaning full learning over cognitive structures). Metacomponents are aids for the representation of thoughts. To develop an efficient, intelligent and optimal agent through the use of metacognition requires the design of a multiple layered control model which includes simple to complex levels of agent action and behaviours. This SMCA model has designed and implemented for six layers which includes reflexive, reactive, deliberative (BDI), learning (Q-Ieamer), metacontrol and metacognition layers

Landscape architecture between politics and science : an integrative perspective on landscape planning and design in the network society

This thesis examines the typical nature of design thinking, which is compared and contrasted with scientific and political thinking. A theretical framework is formulated and applied to landscape planning and design. During the 20th century the established operational orientation in landscape architecture was accompanied by an emerging strategic design approach, referred to as 'research by design'. Two cases of large scale landscape planning and design in the netherlands are given in this publication: the restructuring of sandy soil areas programme and the Stork Plan for Rhine-Meuse floodplain in the central belt of the Netherland

Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives

Agent-based modeling and simulation has evolved as a powerful tool for modeling complex systems, offering insights into emergent behaviors and interactions among diverse agents. Integrating large language models into agent-based modeling and simulation presents a promising avenue for enhancing simulation capabilities. This paper surveys the landscape of utilizing large language models in agent-based modeling and simulation, examining their challenges and promising future directions. In this survey, since this is an interdisciplinary field, we first introduce the background of agent-based modeling and simulation and large language model-empowered agents. We then discuss the motivation for applying large language models to agent-based simulation and systematically analyze the challenges in environment perception, human alignment, action generation, and evaluation. Most importantly, we provide a comprehensive overview of the recent works of large language model-empowered agent-based modeling and simulation in multiple scenarios, which can be divided into four domains: cyber, physical, social, and hybrid, covering simulation of both real-world and virtual environments. Finally, since this area is new and quickly evolving, we discuss the open problems and promising future directions.Comment: 37 page

A SURVEY OF THE PROPERTIES OF AGENTS

In the past decade agent systems were considered to be as one of the major fields of study in Artificial Intelligence (AI) field. Many different definitions of agents were presented and several different approaches describing agency can be distinguished. While some authors have tried to define “what” an agent really is, others have tried to identify agents by means of properties which they should possess. Most authors agree on these properties (at least basic set of properties) which are intrinsic to agents. Since agent\u27s definitions are not consistent, we are going to give an overview and list the properties intrinsic to an agent. Many different adjectives were attached to the term agent as well and many different kinds of agents and different architectures emerged too. The aim of this paper it go give an overview of what was going on in the field while taking into consideration main streams and projects. We will also present some guidelines important when modelling agent systems and say something about security issues. Also, some existing problems which restrict the wider usage of agents will be mentioned too