From b4b1db51077e3db0b8e20fb0ef4ff8a85eebf6c2 Mon Sep 17 00:00:00 2001 From: LSaldyt Date: Mon, 4 Dec 2017 13:39:16 -0700 Subject: [PATCH] Draft progress --- papers/draft.tex | 57 ++++++++++++++++++++++++++++++++++++++---------- 1 file changed, 45 insertions(+), 12 deletions(-) diff --git a/papers/draft.tex b/papers/draft.tex index 3b19bd8..e7e46d1 100644 --- a/papers/draft.tex +++ b/papers/draft.tex @@ -26,21 +26,25 @@ \maketitle \begin{abstract} + [Insert abstract] \end{abstract} +%% This paper stems from Melanie Mitchell's (1993) and Douglas Hofstadter's \& FARG's (1995) work on the copycat program. +%% This project focuses on effectively simulating intelligent processes through increasingly distributed decision-making. +%% In the process of evaluating the distributed nature of copycat, this paper also proposes a "Normal Science" framework. + \section{Introduction} -%% This paper stems from Mitchell's (1993) and Hofstadter's \& FARG's (1995) work on the copycat program. -%% This project focuses on effectively simulating intelligent processes through increasingly distributed decision-making. -%% In the process of evaluating the distributed nature of copycat, this paper also proposes a "Normal Science" framework. -%% -%% First, copycat uses a "Parallel Terraced Scan" as a humanistic inspired search algorithm. -%% The Parallel Terraced Scan corresponds to the psychologically-plausible behavior of briefly browsing, say, a book, and delving deeper whenever something sparks one's interest. -%% In a way, it is a mix between a depth-first and breadth-first search. -%% This type of behavior seems to very fluidly change the intensity of an activity based on local, contextual cues. -%% Previous FARG models use centralized structures, like the global temperature value, to control the behavior of the Parallel Terraced Scan. -%% This paper explores how to maintain the same behavior while distributing decision-making throughout the system. -%% + Copycat's behavior is based on the "Parallel Terraced Scan," a humanistic-inspired search algorithm. +The Parallel Terraced Scan corresponds to the psychologically-plausible behavior of briefly browsing, say, a book, and delving deeper whenever something sparks one's interest. +The Parallel Terraced Scan is a mix between a depth-first and breadth-first search. +To switch between modes of search, FARG models use the global variable \emph{temperature}, which is ultimately a function of the rule strength and the strength of each structure in copycat's \emph{workspace}, another centralized structure. +However, it is not clear a global, unifying central structure like temperature is needed. +In fact, this structure may even be harmful to FARG architectures eventually. +This paper explores the extent to which copycat's behavior can be maintained while distributing decision making. + + Specifically, [] + %% Specifically, this paper attempts different refactors of the copycat architecture. %% First, the probability adjustment formulas based on temperature are changed. %% Then, we experiment with two methods for replacing temperature with a distributed metric. @@ -65,7 +69,7 @@ %% {Efficiency of True Distribution} %% {Temperature in Copycat} %% {Other Centralizers in Copycat} -%% {The Motivation for Removing Centralizers in Copycat} +%% {The Motivation for Removing Centralizers in Coat} \section{Methods} \subsection{Formula Documentation} @@ -78,14 +82,43 @@ As will be discussed in the $\chi^2$ distribution testing section, any copycat formulas without a significant effect will be hard-removed. \subsection{Testing the Effect of Temperature} To begin with, the existing effect of the centralizing variable, temperature, will be analyzed. + As the probability adjustment formulas are used by default, very little effect is had. + To evaluate the effect of temperature-based probability adjustment formulas, a spreadsheet was created that showed a color gradient based on each formula. + [Insert spreadsheet embeds] + Then, to evaluate the effect of different temperature usages, separate usages of temperature were individually removed and answer distributions were compared statistically (See section: $\chi^2$ Distribution Testing). \subsection{Temperature Probability Adjustment} + Once the effect of temperature was evaluated, new temperature-based probability adjustment formulas were proposed that each had a significant effect on the answer distributions produced by copycat. + [Insert formula write-up] \subsection{Temperature Usage Adjustment} + Once the behavior based on temperature was well understood, experimentation was made with hard and soft removals of temperature. + First, a branch of the repository was created where all mentions of temperature were removed. + [Insert nuke write-up] + Then, a branch of the repository (the second revision of copycat to-be) was created, where temperature was removed surgically. + [Insert surgical write-up] \subsection{$\chi^2$ Distribution Testing} + To test each different branch of the repository, a scientific framework was created. + Each run of copycat on a particular problem produces a distribution of answers. + Distributions of answers can be compared against one another with a (Pearson's) $\chi^2$ distribution test. + [Insert $\chi^2$ formula] + [Insert $\chi^2$ calculation code snippets] + \subsection{Effectiveness Definition} + Quantitatively evaluating the effectiveness of a cognitive architecture is difficult. + However, for copycat specifically, effectiveness can be defined as a function of the frequency of desirable answers and inverse frequency of undesirable answers. + Since answers are desirable to the extent that they respect the original transformation of letter sequences, desirability can also be approximated by a concrete metric. + A simple metric for desirability is simply the existing temperature formula, or some variant of it. + So, a given version of copycat might be quantitatively better if it produces lower-temperature answers more frequently. + However, recognizing lower-quality answers is also a sign of intelligence. + So, the extent to which copycat provides poor answers at low frequency and low desirability could be accounted for as well, even though copycat isn't explicitly told to do this. + \section{Results} \subsection{Cross $\chi^2$ Table} + The below table summarizes the results of comparing each copycat-variant's distribution with each other copycat-variant. + [Insert cross $\chi^2$ table] \section{Discussion} \subsection{Distributed Computation Accuracy} + [Summary of introduction, elaboration based on results] \subsection{Prediction} + Even though imperative, serial, centralized code is turing complete just like functional, parallel, distributed code, I predict that the most progressive cognitive architectures of the future will be created using functional programming languages that run distributedly and in true parallel. \bibliographystyle{alpha} \bibliography{sample}