The role of falsification in the development of cognitive architectures: insights from a Lakatosian analysis

It has been suggested that the enterprise of developing mechanistic theories of the human cognitive architecture is flawed because the theories produced are not directly falsifiable. Newell attempted to sidestep this criticism by arguing for a Lakatosian model of scientific progress in which cognitive architectures should be understood as theories that develop over time. However, Newell’s own candidate cognitive architecture adhered only loosely to Lakatosian principles. This paper reconsiders the role of falsification and the potential utility of Lakatosian principles in the development of cognitive architectures. It is argued that a lack of direct falsifiability need not undermine the scientific development of a cognitive architecture if broadly Lakatosian principles are adopted. Moreover, it is demonstrated that the Lakatosian concepts of positive and negative heuristics for theory development and of general heuristic power offer methods for guiding the development of an architecture and for evaluating the contribution and potential of an architecture’s research program

The Grip of History and the Scope for Novelty: Some Results and Open Questions on Path Dependence in Economic Processes

-Path dependence, irreversibility, increasing returns, learning, lock-in.

Human Behavior Models for Agents in Simulators and Games: Part I: Enabling Science with PMFserv

This article focuses on challenges to improving the realism of socially intelligent agents and attempts to reflect the state of the art in human behavior modeling with particular attention to the impact of personality/cultural values and affect as well as biology/stress upon individual coping and group decision-making. The first section offers an assessment of the state of the practice and of the need to integrate valid human performance moderator functions (PMFs) from traditionally separated sub-fields of the behavioral literature. The second section pursues this goal by postulating a unifying architecture and principles for integrating existing PMF theories and models. It also illustrates a PMF testbed called PMFserv created for implementating and studying how PMFs may contribute to such an architecture. To date it interconnects versions of PMFs on physiology and stress (Janis-Mann, Gillis-Hursh, others); personality, cultural and emotive processes (Damasio, Cognitive Appraisal-OCC, value systems); perception (Gibsonian affordance); social processes (relations, identity, trust, nested intentionality); and cognition (affect- and stress-augmented decision theory, bounded rationality). The third section summarizes several usage case studies (asymmetric warfare, civil unrest, and political leaders) and concludes with lessons learned. Implementing and inter-operating this broad collection of PMFs helps to open the agenda for research on syntheses that can help the field reach a greater level of maturity. Part II presents a case study in using PMFserv for rapid scenario composability and realistic agent behavior

Contextualizing generative design

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Architecture, 2004.Includes bibliographical references (p. 107-[110]).Generative systems have been widely used to produce two- and three-dimensional constructs, in an attempt to escape from our preconceptions and pre-existing spatial language. The challenge is to use this mechanism in real-world architectural contexts in which complexity and constraints imposed by the design problem make it difficult to negotiate between the emergent output, the context, and the controllability desired by the human designer. This thesis investigates how generative systems address contextual parameters, including the designer, client, user, meaning, aesthetics, environment, and function. This is demonstrated through my case studies, in which my aim was to avoid computerized unprocessed formalism that does not implicitly allow for any contextual and cultural content. I sought to extend simple algorithmic form-generation processes to allow for the subtleties of a given context to be effectively addressed. Some challenges and questions arose from these case studies. By interrogating different generative machines, common threads and challenges, similar to mine encountered in the case studies, were found. All of the processes that strove towards the creation of a generative system struggled with similar issues: How can we use rule-based systems without sacrificing meaning or function or the humanistic touch? How can we address contextual parameters without a loss?by Saeed Arida.S.M

Executive Attention, Action Selection and Attention-Based Learning in Neurally Controlled Autonomous Agents

I describe the design and implementation of an integrated neural architecture, modelled on human executive attention, which is used to control both automatic (reactive) and willed action selection in a simulated robot. The model, based upon Norman and Shallice's supervisory attention system, incorporates important features of human attentional control: selection of an intended task over a more salient automatic task; priming of future tasks that are anticipated; and appropriate levels of persistence of focus of attention. Recognising that attention-based learning, mediated by the limbic system, and the hippocampus in particular, plays an important role in adaptive learning, I extend the Norman and Shallice model, introducing an intrinsic, attention-based learning mechanism that enhances the automaticity of willed actions and reduces future need for attentional effort required for dealing with distractions. These enhanced features support a new level of attentional autonomy in the operation of the simulated robot. Some properties of the model are explored using lesion studies, leading to the identification of a correspondence between the behavioural pathologies of the simulated robot and those seen in human patients suffering dysfunction of executive attention

A Comprehensive Computational Model of PRIMs Theory for Task-Independent Procedural Learning

The human ability to reason about and learn practically any task has been studied for countless years, but to date we still do not truly understand how human learning is task-independent at the computational level. Researchers have theorized that we can account for many human cognitive behaviors if we combine a task-independent set of primitive procedures with a robust, general learning mechanism that compiles them into cognitive skills for various tasks. The PRIMs theory of procedure learning and transfer is a cognitive architecture theory of human learning that shows how a task-independent set of primitive procedures can support learning in any task that is also supported by the underlying architecture. However, its published architecture implementation, Actransfer, focuses on modeling transfer and does not specify all of the computational details of PRIMs theory. This thesis presents a computationally comprehensive cognitive architecture model of PRIMs theory that I call the PROPs system. I comprehensively define each of the processing steps that PRIMs theory requires and implement these in an agent model using the Soar cognitive architecture. I do this through a methodology for incrementally refining a cognitive architecture model. I use this methodology to extend PRIMs theory and unify it with three-phase learning theory from human performance research, task set theory from psychology and neuroscience, and Soar theory from cognitive architecture research. This achieves several improvements in the model’s ability to replicate human learning behavior. Among the contributions of this work, I introduce a novel form of primitive processing that explains the origins of the primitive procedures of PRIMs theory and supports procedural learning in an unbounded, dynamic working memory space. I show that this improves the model’s ability to match human power-law learning. I also extend Soar cognitive architecture theory with gradual procedural learning in a manner consistent with Soar’s existing theory and introduce a novel computational approach by which a cognitive architecture model can learn to guide automatic long-term declarative memory retrievals based on working memory contents. I finally introduce a novel computational approach by which a model can guide deliberate retrievals through choice-based decision making. In my evaluation of the PROPs system, I identify ways in which PRIMs theory for procedural learning might be further unified with neuroscience theory to broaden the model to include declarative learning. I also identify boundaries where PRIMs models can or cannot currently account for types of human cognitive processing when the models are constrained to be fully task-independent and consistent with the surrounding cognitive architecture. This reveals a path for future cognitive architecture research and development.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169770/1/stearns_1.pd

The Social Cognitive Actor

Multi-Agent Simulation (MAS) of organisations is a methodology that is adopted in this dissertation in order to study and understand human behaviour in organisations. The aim of the research is to design and implementat a cognitive and social multi-agent simulation model based on a selection of social and cognitive theories to fulfill the need for a complex cognitive and social model. The emphasis of this dissertation is the relationship between behaviour of individuals (micro-level) in an organisation and the behaviour of the organisation as a whole (macro-level)