Learning in LIDA

Kugele, Sean; Franklin, Stan

doi:10.1016/j.cogsys.2020.11.001

Cited by 14 publications

(5 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Perceptual Associative Memory might build new connections, Transient Episodic Memory might put together a new event, the Conscious Content Queue adds to the specious present, perhaps Procedural Memory starts building a new scheme, and much more. For a detailed account of learning in LIDA (see Kugele and Franklin, 2021 ).…”

Section: The Learning Intelligent Decision Agent Cognitive Architecturementioning

confidence: 99%

See 1 more Smart Citation

Embodied Intelligence: Smooth Coping in the Learning Intelligent Decision Agent Cognitive Architecture

et al. 2022

Self Cite

View full text Add to dashboard Cite

Much of our everyday, embodied action comes in the form of smooth coping. Smooth coping is skillful action that has become habituated and ingrained, generally placing less stress on cognitive load than considered and deliberative thought and action. When performed with skill and expertise, walking, driving, skiing, musical performances, and short-order cooking are all examples of the phenomenon. Smooth coping is characterized by its rapidity and relative lack of reflection, both being hallmarks of automatization. Deliberative and reflective actions provide the contrast case. In Dreyfus’ classic view, smooth coping is “mindless” absorption into action, being in the flow, and any reflective thought will only interrupt this flow. Building on the pragmatist account of Dewey, others, such as Sutton, Montero, and Gallagher, insist on the intelligent flexibility built into smooth coping, suggesting that it is not equivalent to automatization. We seek to answer two complementary challenges in this article. First, how might we model smooth coping in autonomous agents (natural or artificial) at fine granularity? Second, we use this model of smooth coping to show how we might implement smooth coping in artificial intelligent agents. We develop a conceptual model of smooth coping in LIDA (Learning Intelligent Decision Agent). LIDA is an embodied cognitive architecture implementing the global workspace theory of consciousness, among other psychological theories. LIDA’s implementation of consciousness enables us to account for the phenomenology of smooth coping, something that few cognitive architectures would be able to do. Through the fine granular analysis of LIDA, we argue that smooth coping is a sequence of automatized actions intermittently interspersed with consciously mediated action selection, supplemented by dorsal stream processes. In other words, non-conscious, automatized actions (whether learned or innate) often require occasional bursts of conscious cognition to achieve the skillful and flexible adjustments of smooth coping. In addition, never-conscious dorsal stream information and associated sensorimotor processes provide further online adjustments during smooth coping. To achieve smooth coping in LIDA we introduce a new module to the LIDA cognitive architecture the Automatized Action Selection sub-module. Our complex model of smooth coping borrows notions of “embodied intelligence” from enactivism and augments these by allowing representations and more detailed mechanisms of conscious control. We explore several extended examples of smooth coping, starting from basic activities like walking and scaling up to more complex tasks like driving and short-order cooking.

show abstract

Section: The Learning Intelligent Decision Agent Cognitive Architecturementioning

confidence: 99%

“…Finally, the cognitive cycle in general assists in increasing adaptivity through learning. LIDA agents can update their memory modules with every cognitive cycle ( Kugele and Franklin, 2021 ). In this way, the agent is always slowly but surely moving itself toward a greater degree of adaptivity.…”

Section: Smooth Coping In Lidamentioning

confidence: 99%

Embodied Intelligence: Smooth Coping in the Learning Intelligent Decision Agent Cognitive Architecture

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…An example that addresses the relation of affect with cognition is the work proposed in [95]. In this work the authors propose LIDA, which is composed of an agent architecture (LIDA I ) and agent model (LIDA C ), for cognition, emotion, and learning that is inspired in cognitive science and empirically grounded in cognitive neuroscience.…”

Section: Affect and Memorymentioning

confidence: 99%

From Affect Theoretical Foundations to Computational Models of Intelligent Affective Agents

et al. 2021

View full text Add to dashboard Cite

The links between emotions and rationality have been extensively studied and discussed. Several computational approaches have also been proposed to model these links. However, is it possible to build generic computational approaches and languages so that they can be “adapted” when a specific affective phenomenon is being modeled? Would these approaches be sufficiently and properly grounded? In this work, we want to provide the means for the development of these generic approaches and languages by making a horizontal analysis inspired by philosophical and psychological theories of the main affective phenomena that are traditionally studied. Unfortunately, not all the affective theories can be adapted to be used in computational models; therefore, it is necessary to perform an analysis of the most suitable theories. In this analysis, we identify and classify the main processes and concepts which can be used in a generic affective computational model, and we propose a theoretical framework that includes all these processes and concepts that a model of an affective agent with practical reasoning could use. Our generic theoretical framework supports incremental research whereby future proposals can improve previous ones. This framework also supports the evaluation of the coverage of current computational approaches according to the processes that are modeled and according to the integration of practical reasoning and affect-related issues. This framework is being used in the development of the GenIA3 architecture.

show abstract

“…One of the most relevant consciousness-inspired computational models of cognition is LIDA (Learning Intelligent Decision Agent) [12,13]. The system is based on the Global Workspace Theory (GWT) [14], which is a theory of consciousness considering a working memory of perceptual, evaluative, and attentional contents for activating computational conscious and unconscious processes.…”

Section: Related Workmentioning

confidence: 99%

Smart Sensing: An Info-Structural Model of Cognition for Non-Interacting Agents

Iovane

Fominska²,

Landi

et al. 2020

Electronics

View full text Add to dashboard Cite

This study explores an info-structural model of cognition for non-interacting agents affected by human sensation, perception, emotion, and affection. We do not analyze the neuroscientific or psychological debate concerning the human mind working, but we underline the importance of modeling the above cognitive levels when designing artificial intelligence agents. Our aim was to start a reflection on the computational reproduction of intelligence, providing a methodological approach through which the aforementioned human factors in autonomous systems are enhanced. The presented model must be intended as part of a larger one, which also includes concepts of attention, awareness, and consciousness. Experiments have been performed by providing visual stimuli to the proposed model, coupling the emotion cognitive level with a supervised learner to produce artificial emotional activity. For this purpose, performances with Random Forest and XGBoost have been compared and, with the latter algorithm, 85% accuracy and 92% coherency over predefined emotional episodes have been achieved. The model has also been tested on emotional episodes that are different from those related to the training phase, and a decrease in accuracy and coherency has been observed. Furthermore, by decreasing the weight related to the emotion cognitive instances, the model reaches the same performances recorded during the evaluation phase. In general, the framework achieves a first emotional generalization responsiveness of 94% and presents an approximately constant relative frequency related to the agent’s displayed emotions.

show abstract

Learning in LIDA

Cited by 14 publications

References 47 publications

Embodied Intelligence: Smooth Coping in the Learning Intelligent Decision Agent Cognitive Architecture

Embodied Intelligence: Smooth Coping in the Learning Intelligent Decision Agent Cognitive Architecture

From Affect Theoretical Foundations to Computational Models of Intelligent Affective Agents

Smart Sensing: An Info-Structural Model of Cognition for Non-Interacting Agents

Contact Info

Product

Resources

About