In 1963, architect Cedric Price, theatre producer Joan Littlewood, and cybernetician Gordon Pask proposed a new kind of leisure centre called the Fun Palace. Though never built, the project continues to influence architecture and is the inspiration for this thesis. Known also as the “Laboratory of Fun,” the Fun Palace developed a compelling yet problematic narrative: people would have the freedom to design their own spatial experiences, but their behaviours would be monitored and probed. Innovations from the cybernetic committee had propelled the Fun Palace beyond mundane reality and into the virtual. In fact, the Fun Palace was more than a building; it was an information interface where architecture and humans were connected by cybernetic feedback. Of particular importance to this thesis is the way the Fun Palace antici- pated how digital technology would transform the world, and how it can be understood as an early prototype of the digital city. The model of space that the Fun Palace proposed shifted our understanding of architecture from autonomous and static to complex and dynamic; from an architecture of walls to an architecture of fields. This thesis is organized along three lines of inquiry. Firstly, that architecture is participatory. Secondly, that architecture is multidimensional. Thirdly, that architecture is generated by real-time transactions. The thesis concludes with a speculation called In The Fields: a mobile laboratory for co-creation in the digital city.

This thesis considers cognitive narrative a component of intelligence that specializes in generality. In exploring the ubiquitous external (mediated) and internal (cognitive) functions of narrative it provides two contributions to the literature: a uniquely cross-discipline survey of narratology that bridges humanities, social science, and computational fields; and a theory to generate cognitive personal narratives from ongoing perception. The implications of narrative cognition and cognitive narrative are discussed as well as the limitations of this introductory theory and the grounds for promising future work.

In 1963, architect Cedric Price, theatre producer Joan Littlewood, and cybernetician Gordon Pask proposed a new kind of leisure centre called the Fun Palace. Though never built, the project continues to influence architecture and is the inspiration for this thesis. Known also as the “Laboratory of Fun,” the Fun Palace developed a compelling yet problematic narrative: people would have the freedom to design their own spatial experiences, but their behaviours would be monitored and probed. Innovations from the cybernetic committee had propelled the Fun Palace beyond mundane reality and into the virtual. In fact, the Fun Palace was more than a building; it was an information interface where architecture and humans were connected by cybernetic feedback. Of particular importance to this thesis is the way the Fun Palace antici- pated how digital technology would transform the world, and how it can be understood as an early prototype of the digital city. The model of space that the Fun Palace proposed shifted our understanding of architecture from autonomous and static to complex and dynamic; from an architecture of walls to an architecture of fields. This thesis is organized along three lines of inquiry. Firstly, that architecture is participatory. Secondly, that architecture is multidimensional. Thirdly, that architecture is generated by real-time transactions. The thesis concludes with a speculation called In The Fields: a mobile laboratory for co-creation in the digital city.

Cognitive Grammar is a theory within the framework of Cognitive Linguistics that gives an account of human linguistic ability based entirely on general cognitive abilities. Because of the general complexity and open-endedness of the theory, there is not much computational work associated with it. This thesis proposes that ACT-R cognitive architecture can provide the basic primitives for the cognitive abilities required for a better implementation of Cognitive Grammar. Thus, a language model was developed on the ACT-R architecture. The model processes active and passive sentences, constructs their propositional representations, and tests the representation on a sentence verification task of the experiment of Anderson (1974).

The Same-Different task has a long and controversial history in cognitive psychology. For over five decades, researchers have had many difficulties modelling the simple task, in which participants must respond as quickly and as accurately as possible whether two stimuli are the “Same” or “Different”. The main difficulty in doing so stems from the fact that “Same” decisions are much faster than can be modelled using a single process model without resorting to post-hoc processes, a finding since coined the fast-same phenomenon. In this thesis, I evaluate the strengths and shortcomings of past modelling endeavours, deconstruct the fast-same phenomenon while exploring the role of priming as its possible mechanism, investigate coactivity as a possible architecture underlying both decision modalities, and present an accumulator model whose assumptions and parameters stem from these results that predicts Same-Different performance (both response times and accuracies) using a single-process, a finding deemed near impossible by Sternberg (1998).

The problem of integrated cognition , analyzed in the thesis, belongs to a multi-disciplinary area of cognitive engineering. The multi-disciplinary focusing on cognitive models and real-time embedded systems, such as mobile robots, helps to reveal a broader and deeper understanding of robotics as part of everyday life and society. Over the past decades many cognitive architectures have been proposed and steadily developed, based on different approaches and methodologies, but still current cognitive architectures are far from the goal of covering the requirements for general intelligence. Recent research in the area of evolutionary algorithms and genetic programming is used in this study as an inspiration for developing the new version of integrated cognitive architecture, and the knowledge of human brain structure and functions is applied to the architecture as well. In this study a survey of cognitive architectures is performed, a version of biologically inspired hybrid cognitive architecture is developed. This architecture is influenced by a contemporary research in evolutionary algorithms and genetic programming. Some modules of the architecture are applied to a mobile robot in a simulated environment.

This thesis focuses on modeling Expectation-Violation Effect, which is the superior recall of weakly associated pairs of words over strongly associated pairs. The goal of this thesis is to provide an exploratory computational model. A virtual experiment is conducted based on the datasets used in the psychological experiment by Amster et al. (1992). The computational modeling of this phenomenon is carried in the medium of ACT-R cognitive architecture.

Existing computational cognitive models of spatial memory often neglect difficulties posed by the real world, such as sensory noise, uncertainty, and high spatial complexity. On the other hand, robotics is unconcerned with understanding biological cognition. This thesis takes an interdisciplinary approach towards developing cognitively plausible spatial memory models able to function in realistic environments, despite sensory noise and spatial complexity. We hypothesized that Bayesian localization and error correction accounts for how brains might maintain accurate location estimates, despite sensory errors. We argued that these mechanisms are psychologically plausible (producing human-like behaviour) as well as neurally plausible (implementable in brains). To support our hypotheses, we reported modelling results of neural recordings from rats (acquired outside this PhD), constituting the first evidence for Bayesian inference in neurons representing spatial location, as well as modelling human behaviour data. In addition to dealing with uncertainty, spatial representations have to be stored and used efficiently in realistic environments, by using structured representations such as hierarchies (which facilitate efficient retrieval and route planning). Evidence suggests that human spatial memories are structured hierarchically, but the process responsible for these structures has not been known. We investigated features influencing them using data from experiments in real-world and virtual reality environments, and proposed a computational model able to predict them in advance (based on clustering in psychological space). We have extended a general cognitive architecture, LIDA (Learning Intelligent Distribution Agent), by these probabilistic models of how brains might estimate, correct, and structure representations of spatial locations. We demonstrated the ability of the resulting model to deal with the challenges of realistic environments by running it in high-fidelity robotic simulations, modelled after participants' actual cities. Our results show that the model can deal with noise, uncertainty and complexity, and that it can reproduce the spatial accuracies of human participants.

No description available.

Embodied Conversational Agents (ECA) form part of a range of virtual characters whose intended purpose include engaging in natural conversations with human users. While works in literature are ripe with descriptions of attempts at producing viable ECA architectures, few authors have addressed the role of episodic memory models in conversational agents. This form of memory, which provides a sense of autobiographic record-keeping in humans, has only recently been peripherally integrated into dialog management tools for ECAs. In our work, we propose to take a closer look at the shared characteristics of episodic memory models in recent examples from the field. Additionally, we propose several enhancements to these existing models through a unified episodic memory model for ECA's. As part of our research into episodic memory models, we present a process for determining the prevalent contexts in the conversations obtained from the aforementioned interactions. The process presented demonstrates the use of statistical and machine learning services, as well as Natural Language Processing techniques to extract relevant snippets from conversations. Finally, mechanisms to store, retrieve, and recall episodes from previous conversations are discussed. A primary contribution of this research is in the context of contemporary memory models for conversational agents and cognitive architectures. To the best of our knowledge, this is the first attempt at providing a comparative summary of existing works. As implementations of ECAs become more complex and encompass more realistic conversation engines, we expect that episodic memory models will continue to evolve and further enhance the naturalness of conversations.