Cognitive Architecture Requirements for Achieving AGI

Laird, John E.; Wray, Robert E.

doi:10.2991/agi.2010.2

Cited by 16 publications

(13 citation statements)

References 13 publications

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“…Dieses sogenannte Collaborative Problem Solving, das in vielen Arbeiten im Bereich der Diskursanalyse thematisiert wird 10 , behandelt eine neue Dimension von Aufgaben, die nicht bereits von den Task-Modellen des Gegenstandsbereichs abgedeckt sind. Insbesondere 8 Das wird in der Regel gar nicht möglich sein ohne eine kognitive Theorie, die für jeden Nutzer dessen "interne" kognitive Prozesse aufschlüsseln kann.…”

Section: Kollaboration Und Interaktion Als Eigenständige Domänenunclassified

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Task-Analysen in der Mensch-Maschine-Interaktion

Ludwig

2015

Xpert.press

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Section: Kollaboration Und Interaktion Als Eigenständige Domänenunclassified

“…Der Dialog ist ja nicht Produkt einer tatsächlichen Mensch-MaschineInteraktion, sondern Ergebnis eines Wizard-of-Oz-Experiments. 10 Zu den einschlägigen Arbeiten gehören [19][20][21][22] Abb. 8.3 Concurrent Task Tree, wie M im TRAINS-Szenario interaktiv Pläne konstruiert.…”

Section: Kollaboration Und Interaktion Als Eigenständige Domänenunclassified

Task-Analysen in der Mensch-Maschine-Interaktion

Ludwig

2015

Xpert.press

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“…In addition to providing a framework for collaborative research in AGI, Laird and Wray (2010) identified two significant challenges:…”

Section: Current Challengesmentioning

confidence: 99%

Mapping the Landscape of Human‐Level Artificial General Intelligence

Adams¹,

Arel

Bach³

et al. 2012

AI Magazine

140

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■ We present the broad outlines of a roadmap toward human-level artificial general intelligence (henceforth, AGI). We begin by discussing AGI in general, adopting a pragmatic goal for its attainment and a necessary foundation of characteristics and requirements. An initial capability landscape will be presented, drawing on major themes from developmental psychology and illuminated by mathematical, physiological, and information-processing perspectives. The challenge of identifying appropriate tasks and environments for measuring AGI will be addressed, and seven scenarios will be presented as milestones suggesting a roadmap across the AGI landscape along with directions for future research and collaboration.This article is the result of an ongoing collaborative effort by the coauthors, preceding and during the AGI Roadmap Workshop held at the University of Of course, this is far from the first attempt to plot a course toward humanlevel AGI: arguably this was the goal of the founders of the field of artificial intelligence in the 1950s, and has been pursued by a steady stream of AI researchers since, even as the majority of the AI field has focused its attention on more narrow, specific subgoals. The ideas presented here build on the ideas of others in innumerable ways, but to review the history of AI

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“…The perception of information from the environment is accomplished through a sensor, the agent reasons about the appropriate action to take using some internal model, and the action is expressed in the environment in which the agent is operating (see Figure 2.4). Those agent architectures that attempt to constrain the agent in the same manner that a human would are referred to as cognitive architectures, with adaptive control of thought-rational (ACT-R) and state, operator and result (SOAR) serving as prominent examples (Zacharias et al, 2008;Laird & Wray III, 2010). The internal reasoning employed within these systems can take the form of a rule based expert system, sometimes developed using a goals, operators, methods, and selection rules (GOMS) type of cognitive task analysis, or make use of learning systems that are given only some notion of a goal, a reward, a perception of state, and a set of available actions.…”

Section: Military Decision-makingmentioning

confidence: 99%

Understanding Optimal Decision-Making in Wargaming

Nesbitt¹,

Kennedy²,

Alt³

et al. 2013

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The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, This research aims to gain insight into optimal wargaming decision-making mechanisms using neurophysiological measures by investigating whether brain activation and visual scan patterns predict attention, perception, and/or decision-making errors through human-in-the-loop wargaming simulation experiments. We investigate whether brain activity and visual scan patterns can explain optimal wargaming decision making and its development with a within-person design; i.e., the transition from exploring the environment to exploiting the environment. We describe ongoing research that uses neurophysiological predictors in two military decision making tasks that tap reinforcement learning and cognitive flexibility.

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Cognitive Architecture Requirements for Achieving AGI

Cited by 16 publications

References 13 publications

Task-Analysen in der Mensch-Maschine-Interaktion

Task-Analysen in der Mensch-Maschine-Interaktion

Mapping the Landscape of Human‐Level Artificial General Intelligence

Understanding Optimal Decision-Making in Wargaming

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