Home > Meeting Topics and Material, News Stories > Weekly QUEST Discussion Topics and News, Jan 17

Weekly QUEST Discussion Topics and News, Jan 17

The topics for this week include a discussion on the ideas to answer the question ‘What is QuEST?’ – below is some draft text based on some feedback.
Jargon based answer
QUalia Exploitation of Sensing Technology – QuEST – a cognitive exoskeleton

• QUEST defines and engineers a new set of processes that will be implemented in a computer agent (or set of computer agents) to improve decision quality of a human agent (or set of human agents)

• Assumption 1: decision quality is dominated by the appropriate level of situational awareness

• QUEST could be considered a new approach to situational assessment (processes that are used to achieve situational awareness), situation understanding, or sensemaking (depending on the application) for decision quality over short term versus long term, tactical vs. strategic, and individual versus group conditions

– QUEST agents implement blended dual process cognitive models (have both artificial conscious and artificial subconscious/intuition processes) for situational assessment (processes that are used to achieve situational awareness)

– The artificially conscious processes all are constrained by the fundamental laws of the QUEST Theory of Consciousness (structural coherence, situation based, simulation / cognitively decoupled)
– The subconscious/intuition processes are processes that do not use working memory and thus considered autonomous (do not require consciousness to act) – we believe current approaches to Data driven artificial intelligence provide a wide range of options for implementing instantiations of capturing experiential knowledge.

• QUEST is developing a ‘Theory of Knowledge’ – to provide the theoretical foundations to understand what an agent or group of agents can know which fundamentally changes machine learning from an empirical effort to a scientific effort

Acknowledgement: Over decades of research in perception and recognition from single sources; the information age opens up a distributed set of data, users, and decisions which requires a research group from varied backgrounds to answer fundamental questions of a “situation” for different “agents”.

Street speak answer:

• QUEST improves decision quality by providing decision makers computer based decision aids that are engineered with both intuition and the ability to do deliberative thinking to match results with needs

• QUEST seeks mathematical foundations to understand what can be known by a person or group of people and their computer based decision aids about situations so we can predict when more people (or differently trained people) or more information are necessary to make a particular decision.

The second area for discussion to keep with our goal of weekly exposing the group to an article or area of literature that may be key to delivering QuEST agents – we have two articles we want to vector people to:

Cogn Process (2010) 11:103–121
Good judgments do not require complex cognition
Julian N. Marewski • Wolfgang Gaissmaier •
Gerd Gigerenzer
What cognitive capabilities allow Homo sapiens
to successfully bet on the stock market, to catch balls in
baseball games, to accurately predict the outcomes of
political elections, or to correctly decide whether a patient
needs to be allocated to the coronary care unit? It is a
widespread belief in psychology and beyond that complex
judgment tasks require complex solutions. Countering this
common intuition, in this article, we argue that in an
uncertain world actually the opposite is true: Humans do
not need complex cognitive strategies to make good
inferences, estimations, and other judgments; rather, it is
the very simplicity and robustness of our cognitive repertoire
that makes Homo sapiens a capable decision maker.
Cogn Tech Work (2005) 7:14-28
Gary Klein Æ Rebecca Pliske Æ Beth Crandall
David D. Woods
Problem detection
Abstract Problem detection is the process by which
people first become concerned that events may be taking
an unexpected and undesirable direction that potentially
requires action. Previous accounts [e.g., Cowan (Acad
Manage Rev 11(4):763–776, 1986)] described problem
detection as the accumulation of discrepancies until a
threshold was reached. In reviewing incidents taken
from a variety of natural settings, we found that discrepancy
accumulation did not apply to the incidents we
reviewed, because (a) cues to problems may be subtle
and context-dependent, and (b) what counts as a discrepancy
depends on the problem-solver’s experience
and the stance taken in interpreting the situation. In
many cases, detecting a problem is equivalent to reconceptualizing
the situation.



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