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"Real life tends to cancel out." (Carroll's Razor)
Enos's wrestle "Before God" has many similarities to the Jacob story in the Old Testament. Better understanding the themes and morals of the Jacob story can aid our understanding of the implications and message of the book of Enos. From this comparison we learn that the greatest blessings that God has for us are often only attained after intense spiritual effort on our part.
Draft unavailable because it is currently in submission for publication.
James L. Carroll "An Expanded View of the Israelite Scapegoat"
in Temples and Ritual in Antiquity, presented by the BYU Religious Studies
Center and SANE's Studia Antiqua, 2008.
There have been many divergent interpretations of the scapegoat in LDS
and other Christian commentaries. Some see the scapegoat as a symbol of Christ, others as
a symbol of Satan. In order to better understand the spiritual significance of the scapegoat,
this paper analyzes several parallels that are similar to the Day of Atonement, specifically
rituals and stories involving the elements of death, banishment, release and substitution.
Usually one person or group is put to death, and another person or group is either cast out
or released. It is then possible to understand the first goat (the Lord's goat) and the
second goat (the scapegoat) in terms of physical and spiritual death respectively. Spiritual
death involves banishment or exile, and is a common punishment in the ancient world. It is
also one of the fundamental penalties assigned to sin in the scriptures. If we interpret the
scapegoat in this way, then it does not directly represent Christ or Satan. Rather it represents
a vicarious substitute, suffering the penalty of banishment on Israel's behalf. Such an
interpretation can explain how some have seen the Savior in the scapegoat since he took
spiritual death upon himself for those who repent, while at the same time others have
seen Satan in the scapegoat since he was banished from the Father's presence, thus
suffering spiritual death for his unrepented sins.
The presentations were recorded by the conference and are available at:
Google Video and
YouTube. The paper is currently
unavailable because the draft is currently in submission to the Religious Studies Center for publication.
A preliminary version of some of the ideas in this paper was published in Selections
From the Seventh Annual BYU Religious Education Student Symposium, 2005, and is available below.
James L. Carroll and Stephen Ricks, "Temples of the Ancient World," Brigham Young Education Week Presentation, 2007.
James L. Carroll, "Computer Security for the LDS Family," Brigham Young Education Week Presentation, 2007.
James L. Carroll, "An Interpreters History of the Israelite Three
Room Temple Design," in The Ninth Annual BYU Religious Education Student Symposium, 2007.
In 1842, Joseph Smith revealed the endowment which connected the temple tradition with
the creation and the Garden of Eden. Ancient Israel under the Law of Moses also built
several temples. These temples were not primarily used for endowments as we understand them
today, but were part of an Aaronic order designed to be preparatory in nature. These temples
each contained three main rooms or divisions. We survey several of the most important LDS and
non-LDS theories that attempt to explain the meaning of these rooms and show that even most
non-LDS scholars believe that the Israelite temple design was connected with the creation,
the Garden of Eden, and the fall of man. Thus, although these temples were not identical
to the modern temple pattern revealed through the Endowment, they were designed to teach many
of the same fundamental gospel principles. The majority of these scholarly opinions were given
many years after Joseph revealed the Endowment, illustrating the inspiration of the prophet.
Draft available upon request.
James L. Carroll, "Egyptian Craft Guild Initiations," in Studia Antiqua, The Journal of the Student
Society for Ancient Studies, 2007, vol. 5:1 p. 17-44.
Initiation seems to have played an important role in Egyptian religion from the beginning of recorded history. Initiations are rites whereby the initiate is symbolically moved from one state of being into another or from one part of the temple into another, the passage involves various trials or tests of knowledge, the rites often deal with death and resurrection, various oaths are taken either of an ethical or of a sacramental nature, and the ceremony itself is usually secret. The initiation paradigm can be seen in the Egyptian funerary literature, the Daily Temple Liturgy, the initiations of the Egyptian priesthood, and the later Isis mystery cult initiations. All these ritual elements can also be seen in modern craft guild initiations, however it is unclear how early this paradigm became part of craft guild initiations. Although it can be shown that craft guilds existed in ancient Egypt from the earliest times, little direct evidence of their nature has remained. However, several elements from the earlier Egyptian initiations show evidence of having been influenced by guild initiations. This indicates that the guild traditions may have adopted the initiation paradigm at a very early stage. If this is the case, then it would have significant ramifications for the origins of modern guild initiations, and would indicate that they are connected to ancient traditions of initiation into the afterlife, and to ancient temple traditions.
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James L. Carroll, "The Second
Coming of Christ as Covenant Renewal," in The Eighth Annual
BYU Religious Education Student Symposium, 2006.
The
second coming of Christ, especially his appearance at Jerusalem, can
be thought of as a covenant renewal ritual in which the people at
Jerusalem rediscover the identity of Jehovah, their God, and
recommit their lives to him. This covenant renewal ritual is
similar in form to the ancient covenant patterns found throughout
the ancient Near East and in the Old Testament accounts of Abraham
and Joshua. Viewing the second coming in this manner can help us to
understand several passages that would otherwise be difficult to
interpret, especially the division of the Mount of Olives. In this
context, the division of the Mount of Olives can be seen as a
re-creation of the sacred landscape that was present at Shechem when
the Israelites made their covenant with Jehovah before possessing
the Land of Promise.
Draft available upon request.
James L. Carroll, "A
Revised Temple Typology" in Hagion Temenos, 2nd
Edition ed. Stephen Ricks, Provo Ut. BYU Press, 2005.
John M. Lundquist's "Temple Typology" has been highly influential in the past several years. This "Revised Temple Typology" attempts to build upon what he has created by synthesizing several of Lundquist's publications and by adding several new elements to the typology. Further, the typology is reordered, and organized into three main categories, "the Temple Space," "The Temple Rites," and "The Tempe and Community." Short titles have also been added to the typology elements. It is hoped that these changes will improve the use of the typology in teaching situations.
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James L. Carroll, "An
Expanded View of the Israelite Scapegoat" in Selections
From the Seventh
Annual BYU Religious Education Student Symposium, 2005.
There have been many divergent interpretations of the scapegoat
in LDS and other Christian commentaries. Some see the scapegoat as a
symbol of Christ, others as a symbol of Satan. In order to better
understand the spiritual significance of the scapegoat, this paper
analyzes several scriptural stories that are similar in form to the
Day of Atonement, specifically stories where one person or group is
put to death, and another person or group is cast out or released.
It is then possible to understand the Lord's goat and the scapegoat
in terms of physical and spiritual death respectively. Such an
interpretation can explain how the scapegoat can represent the
Savior, who took spiritual death upon himself for those who repent,
while at the same time representing Satan, who was cast out of the
Father's presence, thus suffering spiritual death for his unrepented
sins.
DOC
James L. Carroll, "The Reconciliation of Adam and Israelite
Temples," in Studia Antiqua, The Journal of the Student Society
for Ancient Studies, Winter 2003.
Modern researchers have shown that ancient temples were often
associated with the creation, the Garden of Eden, and
reconciliation. All three of these elements can be found in Genesis
1-3 if one assumes that Adam and Eve repented of their transgression
in the Garden as many apocryphal elements attest. The methods of
reconciliation that they record form a unifying principal for
understanding the significance of the tripartite divisions found in
Israelite temples which seem to have represented the heavenly throne
of God, the Garden of Eden, and the fallen world where Adam and Eve
worked out their reconciliation with God.
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A survey paper concerning the burning of Incense in its relation to the temple cult of Ancient Israel. We place the burning of incense in its contexts from the religions surrounding Israel, attempt to identify the ingredients mentioned in Ex. 30. We also survey various rabbinic commentaries on the methods for burning incense, and give an LDS interpretation for the meaning of this part of the ancient Israelite temple ritual.
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Kristine Monteith, James L. Carroll, Kevin Seppi, and Tony Martinez, "Turning Bayesian Model Averaging Into Bayesian Model Combination," to appear in The Proceedings of the 2011 International Joint Conference on Neural Networks (IJCNN 2011), San Jose, California July 31 - August 5, 2011.
Bayesian methods are theoretically optimal in
many situations. Bayesian model averaging is generally considered
the standard model for creating ensembles of learners
using Bayesian methods, but this technique is often outperformed
by more ad hoc methods in empirical studies. The
reason for this failure has important theoretical implications
for our understanding of why ensembles work. It has been
proposed that Bayesian model averaging struggles in practice
because it accounts for uncertainty about which model is
correct but still operates under the assumption that only one
of them is. In order to more effectively access the benefits
inherent in ensembles, Bayesian strategies should therefore be
directed more towards model combination rather than the
model selection implicit in Bayesian model averaging. This work
provides empirical verification for this hypothesis using several
different Bayesian model combination approaches tested on
a wide variety of classification problems. We show that even
the most simplistic of Bayesian model combination strategies
outperforms the traditional ad hoc techniques of bagging and
boosting, as well as outperforming BMA over a wide variety of
cases. This suggests that the power of ensembles does not come
from their ability to account for model uncertainty, but instead
comes from the changes in representational and preferential
bias inherent in the process of combining several different
models.
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James L. Carroll,
"A Bayesian Decision Theoretical Approach to Supervised Learning, Selective Sampling, and Empirical Function Optimization," BYU PhD. Dissertation, 2010.
Many have used the principles of statistics and Bayesian decision theory to model
specific learning problems. It is less common to see models of the processes of learning
in general. One exception is the model of the supervised learning process known as the
“Extended Bayesian Formalism” or EBF. This model is descriptive, in that it can describe
and compare learning algorithms. Thus the EBF is capable of modeling both effective and
ineffective learning algorithms.
We extend the EBF to model un-supervised learning, semi-supervised learning, supervised learning, and empirical function optimization. We also generalize the utility model of
the EBF to deal with non-deterministic outcomes, and with utility functions other than 0-1
loss. Finally, we modify the EBF to create a “prescriptive” learning model, meaning that,
instead of describing existing algorithms, our model defines how learning should optimally
take place. We call the resulting model the Unified Bayesian Decision Theoretical Model,
or the UBDTM. We show that this model can serve as a cohesive theory and framework in
which a broad range of questions can be analyzed and studied. Such a broadly applicable
unified theoretical framework is one of the major missing ingredients of machine learning
theory.
Using the UBDTM, we concentrate on supervised learning and empirical function
optimization. We then use the UBDTM to reanalyze many important theoretical issues in
Machine Learning, including No-Free-Lunch, utility implications, and active learning. We
also point forward to future directions for using the UBDTM to model learnability, sample
complexity, and ensembles. We also provide practical applications of the UBDTM by using
the model to train a Bayesian variation to the CMAC supervised learner in closed form, to
perform a practical empirical function optimization task, and as part of the guiding principles
behind an ongoing project to create an electronic and print corpus of tagged ancient Syriac
texts using active learning.
PDF.
James L. Carroll, Neil Toronto, Robbie Haertel, and Kevin D. Seppi
"Explicit Utility in Supervised Learning," to appear in Proceedings of the NIPS 2008 Workshop on Cost-Sensitive Machine Learning, Whistler, British Columbia, Canada.
We use a graphical model of the supervised learning problem to explore
the theoretical effect of utility on supervised learning, No-Free-Lunch, sample
complexity, and active learning. There are two sources of utility that
can affect the above problems: utility that comes from end use and utility
that comes from sample costs. We explore which parts of these problems
depend on utility, and which parts are utility free. Further, we propose a
novel interpretation of the No-Free-Lunch theorems that is independent of utility.
We propose that sample complexity should be redefined in terms of expected
sample costs to achieve a given threshold on expected end use effectiveness
(which would be defined in terms of end use utility). Finally, we explore
the effects of the sample cost function and the end use utility function
on active learning techniques both theoretically (through the optimal
active learning equations) and through several examples including a synthetic
data set and a real life part of speech tagging scenario.
Robbie Haertel, Kevin Seppi, Eric Ringger, James Carroll, "Return on Investment for Active Learning," to appear in Proceedings of the NIPS 2008 Workshop on Cost-Sensitive Machine Learning, Whistler, British Columbia, Canada.
Robbie Haertel, Eric Ringger, Kevin Seppi, James Carroll, Peter McClanahan. "Assessing the Costs of Sampling Methods in Active Learning for Annotation," in the Proceedings of the Conference of the Association of Computational Linguistics (ACL-NAACL: HLT 2008), Columbus, Ohio.
Eric Ringger, Marc Carmen, Robbie Haertel, Noel Ellison, Kevin Seppi, Deryle Lonsdale, Peter McClanahan, James Carroll, "Assessing the Costs of Machine-Assisted Corpus Annotation through a User Study," in the Proceedings of the Language Resources and Evaluation Conference (LREC), May 2008.
James L. Carroll, Robbie Haertel, Peter McClanahan, Eric Ringger, and Kevin Seppi,
"Modeling the Annotation Process for Ancient Corpus Creation," in Chatressar 2007, Proceedings of the
International Conference of Electronic Corpora of Ancient Languages (ECAL), Prague, November 2007.
In corpus creation human annotation is expensive. Annotation costs can be minimized through machine learning and active learning, however there are many complex interactions among the machine learner, the active learning technique, the annotation cost, human annotation accuracy, the annotator user interface, and several other elements of the process. For example, we show that changing the way in which annotators are paid can drastically change the performance of active learning techniques. To date these interactions have been poorly understood. We introduce a decision-theoretic model of the annotation process suitable for ancient corpus annotation that clarifies these interactions and can guide the development of a corpus creation project.
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Eric Ringger, Peter McClanahan, Robbie Haertel, George Busby, Marc Carmen, James Carroll, Kevin Seppi, and Deryle Lonsdale, "Active Learning for Part-of-Speech Tagging: Accelerating Corpus Annotation," in Proceedings of the ACL Linguistic Annotation Workshop, Association for Computational Linguistics, Prague, Czech Republic, June 2007, p. 101-108.
Heal, K., Griffin, C., Ringger, E., McClanahan, P., Carroll, J., Heaton, J., et al., "A Computational Perspective on Syriac Corpus Development and Annotation," presentation given at the XIXth Congress of the IOSOT, the International Organization for the Study of the Old Testament, and ISLP, the International Syriac Language Project, July 2007.
James L. Carroll and Kevin D. Seppi, "No-Free-Lunch and Bayesian Optimality,"
in Meta-Learning IJCNN Workshop 2007.
We take a Bayesian approach to the issues of bias, meta
bias, transfer, overfit, and No-Free-Lunch in the context of
supervised learning. If we accept certain relationships between
the function class, on training set data, and off training
set data, then a graphical model can be created that
represents the supervised learning problem. This graphical
model dictates a specific algorithm which will be the “optimal”
approach to learning the parameters of any given
function representation given the variable relationships.
Thus, there is an optimal technique for supervised learning.
We reconcile this idea of an optimal technique with the
ideas of No-Free-Lunch and show how these ideas relate to
the concepts of meta and transfer learning through hierarchical
versions of the graphical model.
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James L. Carroll, Christopher K. Monson, and Kevin D. Seppi, "A Bayesian CMAC for
High Assurance Supervised Learning," in Applications of Neural Networks in
High-Assurance Systems, IJCNN Workshop 2007.
We analyze the drawbacks to using ANNs in
high assurance systems and propose a solution based upon
a Bayesian approach with a specific network topology that
can be solved in closed form. The Bayesian approach leads
to better answers in the traditional sense, while also allowing
us to quantify risk and deal with it in a reasonable manner. We
demonstrate this approach on several synthetic functions and
the Abalone data set.
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Ringger, E., Carmen, M., Haertel, R., Seppi, K., Lonsdale, D., McClanahan, P., et al., "Assessing the Costs of Machine-Assisted Corpus Annotation Through a User Study," in Proceedings of the 6th Edition of International Conference on Language Resources and Evaluation, Morocco 2007.
Eric Ringger, Peter McClanahan, Robbie Haertel, George Busby, Marc Carmen,
James Carroll, Kevin Seppi, Deryle Lonsdale, "Active Learning for Part-of-Speech
Tagging: Accelerating Corpus Annotation" in Proceedings of the Linguistic Annotation Workshop (LAW 2007), p. 101-108, Prague, Czech Republic: Association for Computational Linguistics.
In the construction of a part-of-speech annotated corpus, we are constrained by a
fixed budget. A fully annotated corpus is required, but we can afford to label only a
subset. We train a Maximum Entropy Markov Model tagger from a labeled subset
and automatically tag the remainder. This paper addresses the question of where to
focus our manual tagging efforts in order to deliver an annotation of highest quality. In
this context, we find that active learning is always helpful. We focus on Query by Uncertainty
(QBU) and Query by Committee (QBC) and report on experiments with several
baselines and new variations of QBC and QBU, inspired by weaknesses particular
to their use in this application. Experiments on English prose and poetry test
these approaches and evaluate their robustness. The results allow us to make recommendations
for both types of text and raise questions that will lead to further inquiry.
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Christopher K. Monson, Kevin D. Seppi, James L. Carroll,
"A Utile Function Optimizer," in The 2007 IEEE Congress of Evolutionary Computation (CEC 2007).
The Evolutionary Optimization DBN (EO-DBN)---a dynamic Bayesian model of optimization---provides a unique opportunity to create an algorithm that more directly addresses the goal of optimization: to carefully select function samples so as to obtain information about the location of its global optimum. As thus described, optimization is fundamentally a decision process and will therefore be addressed using the language and tools of decision theory. Having once cast the problem as a probabilistic network (the EO-DBN), it is possible to create a decision-making agent that uses explicit definitions of utility and cost to rationally select sample locations that maximize information. This work presents and develops this idea, producing a model of optimization and a corresponding algorithm that is optimal with respect to well-stated optimization goals. The algorithm uses naturally expressed domain knowledge to determine where a function should be sampled and when the sampling process should stop, displaying sophisticated behavior when provided with simple specifications.
Available through IEEEXplore.
James L. Carroll Task Localization, Similarity, and Transfer;
Towards a Reinforcement Learning Task Library System, BYU Masters
Thesis, 2005.
This thesis develops methods of task localization, task
similarity discovery, and task transfer for eventual use in a
reinforcement learning task library system, which can effectively "learn
to learn," improving its performance as it encounters various tasks over
the lifetime of the learning system.
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James L. Carroll, Kevin Seppi "Task Similarity Measures for
Transfer in Reinforcement Learning Task Libraries," in The 2005 International Joint Conference on Neural Networks, (IJCNN 2005),
2005.
Recent research in task transfer and task clustering has
necessitated the need for task similarity measures in reinforcement
learning. Determining task similarity is necessary for selective
transfer where only information from relevant tasks and portions of a
task are transferred. Which task similarity measure to use is not
immediately obvious. It can be shown that no single task similarity
measure is uniformly superior. The optimal task similarity measure is
dependent upon the task transfer method being employed. We define
similarity in terms of tasks, and propose several possible task
similarity measures, dT , dP , dQ, and dR which are based on the
transfer time, policy overlap, Q-values, and reward structure
respectively. We evaluate their performance in three separate
experimental situations.
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James L. Carroll, Kevin Seppi "A Bayesian Technique for Task
Localization in Multiple Goal Markov Decision Processes," in The 2004 International Conference on Machine Learning and Applications, (ICMLA 2004).
In a reinforcement learning task library system for Multiple Goal
Markov Decision Process (MGMDP), localization in the task space allows
the agent to determine whether a given task is already in its library in
order to exploit previously learned experience. Task localization in
MGMDPs can be accomplished through a Bayesian approach, however a
trivial approach fails when the rewards are not distributed normally.
This can be overcome through our Bayesian Task Localization Technique (BTLT).
PS PDF
James L. Carroll, Todd Peterson, Kevin Seppi "Reinforcement Learning
Task Clustering (RLTC)," in The 2003 International Conference on Machine Learning and Applications, (ICMLA 2003).
This work represents the first step towards a task library system in
the reinforcement learning domain. Task libraries could be useful in
speeding up the learning of new tasks through task transfer. Related
transfer can increase learning rate and can help prevent convergence to
sub-optimal policies in reinforcement learning. Unrelated transfer can
be extremely detrimental to the learning rate. Thus task transfer is
useful in reinforcement learning if the source task and the target task
are sufficiently related. Task similarity in reinforcement learning can
be determined using many different similarity metrics, and simple
clustering mechanisms can be applied to determine a set of related
tasks. Invariants can be determined among the set of related tasks and
then used in transfer. This paper uses information gathered from a set
of simple grid world tasks to show that clustering of tasks based upon a
similarity metric can be helpful in determining the set of source tasks
which should be utilized in transfer.
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James L. Carroll, Todd S. Peterson "Fixed vs. Dynamic Sub-transfer in
Reinforcement Learning," in The 2002 International Conference on Machine Learning and Applications,(ICMLA 2002).
We survey various task transfer methods in Q-learning and present a
variation on fixed sub-transfer which we call dynamic sub-transfer. We
discuss the benefits and drawbacks of dynamic sub-transfer as compared
with the other transfer methods, and we describe qualitatively the
situations where this method would be prefered over the fixed version of
sub-transfer. We test this method against several other transfer methods
in a simple three room grid world where portions of the source's policy
are relevant to the target task and other portions are not. In this
situation we found that dynamic sub-transfer converged to the optimal
solution, avoiding the sub-optimality inherent in fixed sub-transfer,
while also avoiding some of the convergence problems often experienced
by fixed sub-transfer.
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James L. Carroll, Todd S. Peterson, Nancy E. Owens, "Memory-guided
Exploration in Reinforcement Learning," in The 2001 International Joint Conference on Neural Networks, (IJCNN 2001).
The life-long learning architecture attempts to create an adaptive
agent through the incorporation of prior knowledge over the lifetime of
a learning agent. Our paper focuses on task transfer in reinforcement
learning and specifically in Q-learning. There are three main model free
methods for performing task transfer in Q-learning: direct transfer,
soft transfer and memory-guided exploration. In direct transfer Q-values
from a previous task are used to initialize the Q-values of the next
task. Soft transfer initializes the Q-values of the new task with a
weighted average of the standard initialization value and the Q-values
of the previous task. In memory-guided exploration the Q-values of
previous tasks are used as a guide in the initial exploration of the
agent. The weight that the agent gives to its past experience decreases
over time. We explore stability issues related to the off-policy nature
of memory-guided exploration and compare memory-guided exploration to
soft transfer and direct transfer in three different environments.
PDF
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Todd S. Peterson, Nancy E. Owens, and James L. Carroll, "Towards
Automatic Shaping in Robot Navigation," in The 2001 IEEE International Conference on Robotics
and Automation (ICRA, 2001).
Shaping is a potentially powerful tool in reinforcement learning
applications. Shaping often fails to function effectively because of a
lack of understanding about its effects when applied in reinforcement
learning settings and the use of inadequate algorithms in its
implementation. Because of these difficulties current shaping techniques
require some form of manual intervention. We examine some of the
principles involved in shaping and present a new algorithm for automatic
transferral of knowledge which uses Q-values established in a previous
task to guide exploration in the learning of a new task. This algorithm
is applied to two different but related robot navigation tasks.
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Dr. J. Bart Czirr, David B. Merrill, David Buehler, Thomas K.
McKnight, James L. Carroll, Thomas Abbott, Eva Wilcox, "Capture-gated
Neutron Spectrometry," in Nuclear Instruments and Methods A, June
2000.
Paper unavailable.