the vanguard project overview

8/14/2019 The Vanguard Project Overview

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Chris Frueh

Psychology 32110.26.2008

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In the history of psychology, riddled as it is with vague or contradictory

definitions espoused by one school of thought or another, few concepts have been less

clear than what is generally called intuition. Osbeck, in particular, explored the historical

blurriness of the topic in her 1999 paper. However, with the strong foundation set by

Rosenblatt and Thickstun and built upon with Osbecks research, a consistent definition

has arisen that bears exploring. I plan to show, in brief, how intuition, and specifically the

subset of decision-making automatic thought, is a process strongly rooted in both

memory and the pattern-recognition and comparison processes.

Rosenblatt and Thickstun stated in their 1994 article Intuition and

Consciousness that intuition functions as a form of unconscious pattern-matching

cognition (Rosenblatt 697). They elaborate further as they say that this pattern-

matching functions as a currently perceived pattern is matched to a stored pattern.

The result of this activity may become conscious when a match is achieved.

(Rosenblatt 700) It is reasonable then to conclude that as an observer recognizes a

situation similar to a previously encountered event, he might recall details of his

decisions made and the results thereof. These recollections and comparisons may not

even be fully conscious as one learn[s] from experience without awareness of doing

so. (Osbeck 231)

In an attempt to explain in detail how this might work, one would need a mind

just as capable of memory as a humans would, if not more so, and be able to do a

significantly large number of parallel comparative processes:


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Chris Frueh


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The computer and the human mind have different but complementary abilities.

The computer excels in analysis and numerical computation, and human excels in

pattern recognition, the assessment of complicated situations and intuitive leaps to

new situations. (Gill 9)

Given this recognition of the superior calculative ability of the computer, a revelation

hardly new, one need only translate the identifying characteristics of a situation into

symbols a computer can manipulate and design an adaptive protocol allowing the

computer to somewhat mimic human memory and learning processes.

To demonstrate in thought experiment form how such a monumental task would

work, consider a digital computer game which I happen to pay in which a method for

reading and recording the situational characteristics already existed. If one were set in a

game such as this built around a simultaneously co-operative and counter-operative

strategy and tactics framework, the foundation for this computers adaptation would

nearly complete. To allow one to visualize this, a brief elaboration is required. While the

game itself is unimportant to the workings of the intuition model, an understanding of the

mechanics of the example might allow one a more vivid picture of the workings of the

model itself.

The game in question is a conflict between two teams. There are definable

objectives, resources that may be expended, clear positional variables, a numerical health

system, and an overt win condition. There is also a subtle hierarchy of importance

between these various objectives and resources: momentum may be sacrifices to attain an

objective or an objective may be surrendered to make victory certain. A human player


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Chris Frueh


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engaged in such a match examines and evaluates all of this at extremely rapid speeds,

speeds beyond the scope of self reporting. Now the symbolic nature of a players thought

may be understood in relation to Herbert Simons claim that [human and computer]

systems are symbol systems. They achieve their intelligence by symbolizing external and

internal situations and events, and by manipulating these symbols. (Norman 16)

Now, in constructing an artificial intelligence to play this game using the

memory-based intuition, the program needs to store the quantified characteristics of

situations it encounters as well as the actions taken and the result. Only by recognizing

future situations by their introductory characteristics will the computer be able to

remember previously successful tactics in the same way that I might watch match

footage from previous games to learn tactics from players better than myself.

Next, the computer would monitor the current situation of the game it is playing

and compare it with previously encountered situations. Normans book elaborates this

rather well:

The principle mechanism of intelligence that we have observed operating in

problem environments is heuristic search. The search part of the heuristic

search process is obvious enough. What are more subtle are the heuristic search

devices that enable an intelligent system to carry on searches with great selectivity

by (1) using information stored in memory to choose more promising over less

promising paths, and (2) extracting from the problem environment new

information about regularities in its structure that can similarly guide the search.

(Norman 19)


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Chris Frueh


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These subtleties are exactly what the deliberative nature and hyper-accurate memory of

the computer will bring forth as it scans through previous scenarios and compares the

relevance of each historical scenario with the current in real time. In doing so, the

computer is constantly discarding irrelevant tactics as the fluid situation changes. Thus,

when a situation presents an opportunity to advance to a more advantageous resolution,

the computer will work to maximize the minimum amount of gain like the MaxiMin

protocol. The computer will enact the most successful tactic that most fits the current

situation after analyzing the usefulness of the maneuvers results to the strategic goals of

the team.

Given these two steps so far, the computer can watch every moment of any game

and save the tactics used for future situations calling them up again as the situation

warrants. All that is required is an adaptive coda. As each strategy is acted out over a

theoretical infinite number of games, the computer reanalyzes each strategys

successfulness and re-categorizes each appropriately.

To relate this back to intuition, the human mind acts in a similar way, albeit with

more rapid leaps and with less recognition of the process workings. While, admittedly,

an inexperienced individual may have insight into the nature of a problem, an elder

person has the experience to recall previously pitfalls or successes and act accordingly, a

concept embodied in the phrase seen the world. This model would, as an experienced

player would, learn from past mistakes even if he himself had not made them and would

update his methods based on real-time results.


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Chris Frueh


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In summary, a memory-based intuition model is logically consistent and previous

research supports the framework upon which it is based. A person acting in this intuitive

fashion in a similar automatic decision would recognize the various pressures and choices

and, recollecting a previous experience, would act in such a way to either avoid or

promote the previous outcome. In this light, perhaps future developments in computer

languages might allow further detailed enactments of decision-making processes that

would confirm or deny the hypotheses presented here.

the vanguard project overview

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