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The reactive and deliberative layers differ in that the deliberative layer evolved much later and 
requires a far more sophisticated long-term memory, as well as symbolic reasoning capabilities using a 
short-term reusable memory. The meta-management layer may have evolved at a still later time and 
requires explicit use of concepts referring to states of an information processing architecture. The earliest 
organisms, such as most existing organisms, were totally reactive. Deliberative and meta-management 
layers evolved later. Adult humans appear to have all three types of processing, which is probably rare 
among other animals. 
One of the key features that gives 11-Cogaff its generality is the fact that different components, instead 
of forming parts of simple pipelines, can concurrently send information of various kinds to arbitrarily many 
other components, allowing a wide variety of feedback mechanisms and triggering mechanisms. 
In story understanding, the meta-management level may control the deliberative level in a number of 
ways. 
* If the deliberative level is spending too much time considering certain details and those details are 
not crucial to the story, the meta-management level will make the deliberative level stop. 
* If the deliberative level is spending too much time on a task that does not relate to the goal of 
reading the story, the meta-management level will make the deliberative level stop. 
* If the deliberative level becomes confused, the meta-management level will tell it to go back and 
reread. The deliberative level may have ruled out a possibility earlier that needs to be reconsidered in light 
of new information. 
Minsky further elaborates the H-Cogaff architecture into the six-level architecture called "Model Six" 
shown in Figure 2. (1) At its bottom lies a "zoo of instinctive subanimals" built upon ancient, ancestral 
systems that still maintain our bodies and brains. These include systems for feeding, breathing, heating, 
sleeping, and other systems that keep us alive. The deliberative and reflective levels are engaged to solve 
more difficult kinds of problems. The self-reflective level is engaged when the problems involve our 
relationships with our past and future selves. At the top lies machinery that we acquire from our societies. 
such as suppressors and censors, imprimers and values, and our various kinds of self-ideals. 
[FIGURE 2 OMITTED] 
Multiple reasoning and representation schemes and levels. An architecture of diversity would embed 
representations from natural language to micronemes (27,1) as depicted in Figure 3. The representations 
depicted include frames, transframes, frame-arrays, K-lines, and micronemes. A frame is a representation 
based on a set of slots to which other structures can be attached. (28) Each slot is connected to a default 
assumption that is easily displaced by more specific information. A transframe is a particular type of frame 
representing the causal trajectory between the initial and resulting states representing a situation that a legal 
action was performed on. A frame-array is a collection of frames that share the same slots, making it easy 
to change perspective with respect to physical viewpoint or other mental realms. A knowledge-line or K-
line is a wirelike structure that attaches itself to whichever resources are active in solving a problem. The 
K-line simplifies activation of those same resources when solving a similar problem in the future. 
Micronemes are low-level features for representing the many cognitive shades and hues of a context. In 
Figure 3, new evolved structures are made from older lower-level ones, and the tower shown might be a 
plausible Darwinian brain-development scheme. 
[FIGURE 3 OMITTED) 
Table 2 shows just a few of the diverse representation and reasoning schemes useful for domains of 
story understanding. 
We propose to address the commonsense reasoning problem starting with stories for very young 
readers. However, to demonstrate all of the different ways we think when understanding a story, and what 
we would eventually expect a commonsense story understanding system to be able to handle, consider the 
following adult story (the discussion here is condensed from Reference 1). 
Joan heard a ring and picked up the phone. Charles was answering her 
question about how to use a certain technique. He suggested she read a 
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certain book, which he would soon bring to her since he had planned to be 
in her neighborhood. Joan thanked hint and ended the call. Soon Charles 
arrived and gave her the book. 
Following are a few of the understandings an adult reader would have after hearing the story. 
• Joan heard a ring. She recognizes it as a telephone bell and feels the need to respond quickly. She 
knows how to use the telephone. 
• She picked up the phone. She is subsequently holding the phone to her ear. 
* Charles was answering her question. Charles and Joan arc not in the same room. Charles also knows 
how to use the telephone. 
• He suggested she read a certain book. Joan probably now feels some relief, since she knows where 
to fmd the knowledge she needs. 
* He had planned to be in her neighborhood. Joan will not be surprised when lie arrives, because she 
will remember that he said he would come. 
* He gave her the book. Will she have to give it back? The story does not tell us that. 
These conclusions are based on reasoning and representations in many realms, as follow. 
The physical realm. In this realm, give might mean the motion of the book through space. This could 
be represented as a transframe that starts with Charles's hand holding the book and ends with Joan's hand 
carrying it. One must know a lot about physical things and how they behave in space and time. 
The social realm. In this realm, give may signify social acts that can alter the relationships of the 
actors. What were Charles's motives or his attitudes? Clearly, he was not returning a loan. Was he hoping to 
ingratiate himself? Or was he just being generous? How will Joan feel about Charles after he gives her the 
book? One must know a lot about what people are. and a certain amount about how people work. 
The dominion realm. Given Charles gave Joan the book, one infers not only that Joan is holding the 
book, but also that, at least for a time, she possesses the right to use it. 
The conversational realm. How do conversations work? Consider how many elaborate skills arc 
involved in a typical verbal exchange. One has to keep track of what is being discussed, what one has 
previously told the listener, and what the listener knows. Thus conversations are partly based on knowledge 
of how human memories work and what is commonly known in one's culture. One has to make sure the 
listener has understood what was said and why it was said. One certainly needs to know how to speak and 
to understand some of what one may hear. 
The procedural realm. How does one make a telephone call? One must first find a phone and dial a 
number. Then once the connection has been established, one says hello, talks a bit, and eventually leads 
into why one called. At the end, one says goodbye and hangs up the phone. Generally, such scripts have 
certain steps that are specified, while other steps provide for more room to improvise. 
The sensory and motor realms. Each of the above steps raises questions. For example, it takes only 
one second or so for one's arm to reach out in order to pick up the phone. How can one do that so quickly? 
The kinesthetic, tactile, and haptic realms. Using a telephone or any other physical object engages a 
great base of body-related knowledge and skills. One anticipates how the phone will feel against one's car 
or sandwiched between shoulder and cheek. One expects certain haptic sensations such as the feel of the 
phone's weight. One strengthens one's grip when the phone starts to slip. 
The temporal realms. People have elaborate models of time where events are located in futures and 
pasts that are represented in relation to other times and events or in anecdotal stories. 
The economic realm. People know and reason about the costs incurred by each action or transaction in 
terms of money, energy, space, or time. 
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The reflective realm. People know about themselves. One knows to some degree what one can or 
cannot do, what kinds of problems one can solve, how one's thinking and memory works, and what sorts of 
things one is able to learn. 
Along with these positive kinds of knowledge, one also has negative knowledge about what might go 
wrong when using a phone. One must know what to do if one gets a wrong number, if there is no answer. 
or if a modem or intercept recording is reached. 
Example system with architecture of diversity. Thus far, the Sloman and Minsky architectures arc 
theoretical constructs and have not yet been implemented. However, there are examples of working 
systems that capture the spirit of such architectures. One such example is the M system depicted in Figure 
4. (29) M integrates multiple reasoning processes and representations to serve as an assistant to a user 
collaborating with other workers within a virtual meeting room that hosts multimedia desktop 
conferencing. M serves to recognize and classify the actions performed by the participants as well as the 
objects upon which the actions are applied; example actions and objects are brainstorming on a whiteboard, 
coauthoring a document, and creating and working with other artifacts. 
[FIGURE 4 OMITTED) 
Next steps 
The two recent meetings held in March 2002 at the IBM Thomas J. Watson Research Center and in 
April 2002 on St. Thomas indicate that there is a dedicated group of recognized researchers interested in 
working together on a project to develop a solution to commonsense reasoning. We are now planning to 
undertake some of the next steps in a plan for such a project. The inspiration for this work comes from 
Minsky's past and forthcoming work. We close with his thoughts on how such a project might be realized, 
as follows. 
Our goal is to aim toward a critical "change of phase" that will come when we cross a threshold at 
which our systems know how to improve themselves. This is something that all young children can do, but 
we do not know enough about how they do it; so one goal of the project must be to develop better models 
of how normal people think. 
We will start by trying to implement some of the architectures proposed over the past decade. There 
already exist many useful schemes for representing and using knowledge mostly of a factual nature for use 
on what we call the deliberative level. However, there has not been enough work on the higher reflective 
and self-reflective levels that humans use, as they learn to improve their thinking itself. Any such system, 
we claim, will need additional kinds of meta-resources, which will include systems that manage, criticize, 
and modify the already operating parts of the structure. 
In the field of Al we already have many resources related to this, for example, neural networks, formal 
logic, relational databases, genetic programs, statistical methods, and of course the heuristic search, 
planning, and case-based reasoning schemes of earlier years. However, our goal is not to discuss which 
method is best. Instead we will try to develop a plan of how to incorporate into one system the virtues of 
many different approaches. Of course, each such scheme has deficiencies and our hope is that our system 
can escape from these by using higher-level, more reflective schemes that understand what each of those 
other schemes can do and in what context they are most effective. 
Table 1 Early reader corpus: top 10 domains of common 
sense 
Domain 
Number 
Percentage 
of Stories of Stories 
space--location 
14 
93.3 
space--motion 
11 
73.3 
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606 of 1456 DOCUMENTS 
Copyright 2002 Gale Group, Inc. 
ASAP 
Copyright 2002 All Rights Reserved. 
IBM Systems Journal 
September 1, 2002 
SECTION: No. 3, Vol. 41; Pg. 530; ISSN: 0018.8670 
IAC-ACC-NO: 91469723 
LENGTH: 6160 words 
HEADLINE: An architecture of diversity for commonsense reasoning; Technical forum. 
BYLINE: McCarthy, J.; Minsky, M.; Sloman, A.; Gong, L.; Lau, T.; Morgenstern, L.; Mueller, E-.T.; 
Riecken, D.; Singh, M.; Singh, P. 
BODY: 
Although computers excel at certain bounded tasks that arc difficult for humans, such as solving 
integrals, they have difficulty performing commonsense tasks that are easy for humans, such as 
understanding stories. In this Technical Forum contribution, we discuss commonsense reasoning and what 
makes it difficult for computers. We contend that commonsense reasoning is too hard a problem to solve 
using any single artificial intelligence technique. We propose a multilevel architecture consisting of diverse 
reasoning and representation techniques that collaborate and reflect in order to allow the best teclutiques to 
be used for the many situations that arise in commonsense reasoning. We present story understanding--
specifically, understanding and answering questions about progressively harder children's texts--as a task 
for evaluating and scaling up a commonsense reasoning system. 
In the fall of 2001, a proposal was developed by Marvin Minsky, Erik Mueller, Doug Riecken, Push 
Singh, Aaron Sloman, and Oliver Steele for a project to develop a human-level commonsense reasoning 
system. The basic proposal was (1) to develop certain ideas of Minsky and Sloman about a multilevel 
cognitive architecture, and (2) to develop the system in a way that would exploit many existing artificial 
intelligence techniques for commonsense reasoning and knowledge representation, such as case-based 
reasoning, logic, neural nets, genetic algorithms, and heuristic search. 
We proposed to organize a meeting at which we would bring together many of the major established 
researchers in the area of commonsense knowledge and reasoning. Riecken organized a preliminary 
meeting at the IBM Thomas J. Watson Research Center in March 2002, at which many IBM researchers 
were invited to discuss and react to this general subject as well as to present their own ideas. Afterwards, 
the specific proposal was discussed in more detail by specialists in commonsense knowledge and reasoning 
at a meeting held on St. Thomas, Virgin Islands, in April 2002, and hosted by Jeffrey Epstein. This 
Technical Forum contribution focuses on the preliminary meeting, but also contains some material 
presented at the April meeting, including some material from Minsky's forthcoming book The Emotion 
Machine. (1) 
At the IBM meeting, a broad consensus was reached on three main points. First, there was agreement 
that the community should strive toward solving a nontrivial problem that would require a level of 
knowledge, and a capability of reasoning with that knowledge, beyond what is demonstrated by current 
systems. The problem put forward was that of story understanding. An important advantage of the story 
understanding task is that standardized tests are available to evaluate students on their reading 
comprehension skills. Moreover, these tests require the use of commonsense reasoning skills. It is thus 
possible to evaluate the performance of any story understanding system against that of students at different 
reading levels. (2) 
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Second, there was consensus that the story understanding task provides a strong testbed for evaluating 
a commonsense reasoning system. Not only does such a system need several different forms of reasoning, 
representation, and learning, but it also needs them to work in conjunction with each other. In addition, the 
task highlights the importance of using and reasoning with common sense. This is illustrated by a sentence 
from a story about a child and her grandfather: "He gently takes my elbow as we walk so that I can help 
show him the path." Knowledge of the fact that the grandfather is blind, and the commonsense facts that 
people ordinarily use their sight to find paths and that blind people are unable to see, enable the inference 
that the child is guiding the grandfather and not merely pointing out the path, another frequent sense of the 
word "show." Absence of this commonsense knowledge could lead to the incorrect interpretation of the 
word "show." 
Third, there was agreement on the need to develop a testbed architecture for representation and 
reasoning that allows different systems and representations to work with each other. Researchers often try 
to solve a problem using just one form of representation and reasoning. But such an approach does not 
work well for sufficiently complex problems such as story understanding. In contrast, enabling various 
techniques to collaborate will allow the best techniques to be used for a given situation. Any such 
architecture must provide metalevel control and knowledge that will enable different techniques to 
determine whether or not they are suited for a given task, to decide what other techniques may be better for 
the task, and to communicate information and share partial results with each other. 
What makes commonsense reasoning difficult 
Commonsense reasoning--the sort of reasoning we would expect a child to do easily--is difficult for 
computers to do. Certainly, the relative paucity of results in this field does not reflect the considerable 
effort that has been expended, starting with McCarthy's paper "Programs with Common Sense." (3) 
Nevertheless, the problem remains unsolved. What is it about commonsense reasoning that makes it 
difficult to automate? Various explanations have been suggested, some of which we discuss in this section. 
McCarthys commonsense informatic situation. The knowledge needed to solve a commonsense 
reasoning problem is typically much more extensive and general than the knowledge needed to solve 
difficult problems. McCarthy points out that the knowledge needed to solve well-formulated problems in 
fields such as physics or mathematics is bounded. (4) In contrast, there are no a priori limitations to the 
facts that are needed to solve commonsense problems: the given knowledge may be incomplete; one may 
have to use approximate concepts and approximate theories; one will generally have to use non-monotonic 
reasoning to reach conclusions; and one will need some ability to reflect upon one's own reasoning 
processes. Morgenstern provides an example of the commonsense informatic situation in the problem of 
two friends arranging to meet for dinner at a restaurant. (5) 
Explicit vs implicit knowledge. Commonsense knowledge is often implicit, whereas the knowledge 
needed to solve well-formulated difficult problems is often explicit. For example, the knowledge needed to 
solve integrals can be found in explicit form in a standard calculus textbook. However, the knowledge 
needed to arrange a dinner meeting exists in vague, implicit form. Implicit knowledge must rust be made 
explicit, which is a time-consuming task requiring a serious knowledge engineering effort. 
Domain knowledge. A huge amount of knowledge is needed to do even simple forms of 
commonsense reasoning. For example, to figure out what sorts of objects will work as stakes in a garden--a 
reasoning task that seemingly demands no effort--requires knowledge of plant materials, how plants grow, 
flexibility and hardness, shapes of plants, soil texture, properties of wind, spatial reasoning, and temporal 
reasoning. (6) Although there have been a number of efforts to capture large amounts of world knowledge, 
most notably the Cyc ** project, (7) we are not at this point aware of any knowledge base that contains the 
information necessary to reason about stakes in a garden or about fumbling for an object in one's pocket. 
This Technical Forum piece does not present a solution to these difficulties. Rather, we are attempting 
to see how far we can progress on an importmit commonsense reasoning problem even in the presence of 
such difficulties. 
Story understanding as a vehicle for studying commonsense reasoning 
Story understanding requires addressing the commonsense informatic situation. A story understanding 
system should be able to read and understand a story, and demonstrate its understanding by (1) answering 
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questions about the story, (2) producing paraphrases and summaries of the story, and (3) integrating the 
information the story contains into a database. Further, useful results from this work will have a direct 
impact on many business products and services. 
A brief history of story understanding systems. Starting in the 1960s, (8) researchers have studied 
story understanding and have built systems that can read and answer questions about simple stories. An 
early system built by Charniak (9) used a single mechanism, test-action demons, for making inferences in 
understanding. In the 1970s, Schank and Abelson (10) proposed scripts, plans, and goals as knowledge 
structures for understanding. These knowledge structures were incorporated into the SAtvi (11) and PAM 
(12) story understanding systems. 
In the 1980s, knowledge structures for emotions, story• themes, and spatialitemporal maps were 
incorporated into BORIS. (13) AQUA (14) used case-based reasoning to retrieve and apply explanation 
patterns in order to answer questions raised by anomalies encountered while reading a story. CRAM (15) 
used a connectionist approach to story understanding. 
Recent story understanding systems have adopted the approach of understanding a story by building 
and maintaining a simulation that models the mental and physical states and events described in the story, 
as demonstrated in ThoughtTreasure. (16) The advantage of this approach is that it is easy to answer 
questions about the story simply by examining the contents of the simulation. 
Critical problems for story understanding systems. The story understanding systems built so far work 
only on the particular stories they are designed to handle. For example, SAM (11) handles five stories, 
BORIS (13) three, AQUA (14) five, and ThoughtTreasure (16) three. What prevents story understanding 
systems from scaling up to hundreds of previously unseen stories? 
We contend that story understanding research is blocked on three critical problems: (I) complexity of 
the structure of natural language, (2) necessity for large commonsense knowledge bases, and (3) 
combinatorial explosion in the understanding process. 
Complexity of the structure of natural language. Rare is the simple subject-verb-object sentence that 
maps into a simple proposition. More typically, text contains numerous language phenomena such as 
adverbials, compound nouns, direct and indirect speech, ellipsis, genitive constructions, and relative 
clauses. (17) Present-day syntactic and semantic parsers have trouble producing accurate parses of typical 
story sentences. 
Necessity for large commonsense knowledge bases. Understanding even simple stories requires 
knowing a huge number of facts. For example, understanding the first paragraph of The Cat in the Hat 
requires knowing about children's play, how children can be affected by winter wea
heir relationship 
to their parents, and notions of discipline, boredom, surprise, and risk. Similarly, as 
(18) points out, 
the first paragraph of The Tale of Benjamin Bunny assumes familiarity with concepts of quantity, space, 
time, physics, goals, plans, needs, and communication. 
Combinatorial explosion in the understanding process. Multiple possible interpretations arise at all 
levels of language. Words are ambiguous as to part of speech and word sense. Sentences are syntactically 
ambiguous. There arc several possible explanations for any action of a story character, several possible 
explanations for those explanations, and so on. We get a combinatorial explosion: the understanding 
process must search an extremely large space of possibilities. 
Approaches to critical problems in story understanding. What can be done? We propose a three-
pronged approach. First, to deal with the complexity of the structure of natural language, we make a major 
cut in complexity by going back to books for early readers. Second, to deal with the necessity for large 
commonsense knowledge bases, we propose to identify the domains most frequently used in a restricted set 
of stories and to address these first. Last, to deal with the combinatorial explosion in the understanding 
process, we propose a new paradigm for commonsense reasoning: an architecture of diversity. 
Early readers. Early reader texts are designed for preschool and kindergarten students. These texts 
employ a small or controlled vocabulary, short sentences, and limited language constructions. Working 
with early reader texts will enable us to effectively solve the language front-end problem using existing 
research techniques. 
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Text annotation for domain identification. We cannot hope to deal with the commonsense informatic 
situation head-on. The point of McCarthy's 1996 paper (4) is that any domain can be relevant to a particular 
problem: when reading a story, any area of knowledge may be necessary for comprehension. This is less 
true for stories designed for very young readers; although, as our examples above show, a great many 
concepts and domains are still needed for full comprehension even of early reader texts. Nevertheless, we 
believe we can make progress by choosing to address those domains that most frequently turn up in 
children's stories. Such an approach would, we hope, make the problem tractable. 
We thus propose the following corpus-based approach. We start with a corpus of stories at the 
preschool and kindergarten levels and divide the corpus into a development set and a test set. We manually 
annotate each story in the development set with an informal inventory of what domains of commonsense 
knowledge and reasoning must be addressed in order to understand the story. We sort the domains by their 
frequency and attempt to develop methods to understand the domains that occur most frequently. We start 
with the most frequent domain, proceeding to the next most frequent domain, and so forth Development 
proceeds on the development set, and a final evaluation of the generality of the system is conducted on the 
previously unseen test set. We iterate this process on successively higher reading levels, progressing to 
stories designed for Grades 1, 2, and 3. This approach, based on an incremental series of experiments, will 
enable a significant research focus at each step on an architecture of diversity. 
To demonstrate how this approach would work, we formed a corpus of 15 early reader stories and 
annotated them as to the domains of common sense necessary for understanding them. The vocabulary size 
was 561 words. The top 10 domains of common sense are shown in Table I. This provides us with a path 
for research in understanding the story corpus: focus on handling the most frequently appearing domains of 
common sense. 
Dealing with these concepts is by no means trivial. We plan to leverage the extensive work that has 
been done in these areas. Such work includes: ThoughtTreasure, (16) NETL2, (19) Cyc, (7) Shanahan's 
formalization of time, (20) the RCC formalization of space, (21) and Kuipers's Spatial Semantic Hierarchy. 
(22) We will also employ rapid knowledge formation techniques such as Open Mind. (23) 
An architecture of diversity 
Many attempts to build intelligent computers have hunted for a single mechanism (such as universal 
sub-goaling, propagation rules, logical inference, probabilistic reasoning) or representation (such as 
production rules, connectionist networks, logical formulas, causal networks) that would serve as a basis for 
general intelligence. Why have these approaches so far failed to achieve human-level common sense? 
We believe that the problem is too large to solve using any single approach. Human versatility must 
emerge from a large-scale architecture of diversity in which each of several different reasoning 
mechanisms and representations can help overcome the deficiencies of the other ones. (24,1) Our 
hypothesis is that such an architecture can overcome the combinatorial explosion problem in story 
understanding. 
Multilevel cognitive architecture. We conjecture that the information processing architecture of a 
human is something like the three-level architecture developed by Sloman in the Cognition and Affect 
project (25) (H-Cogaff), shown in Figure 1. This conjecture is based on evidence of many kinds from 
several disciplines, and constraints on evolvability, implementability in neural mechanisms, and 
functionality. (26) 
[FIGURE 1 OMITTED] 
Reactive processes are those in which internal or external states detected by sensors immediately 
trigger internal or external responses. Deliberative processes are those in which alternative possibilities for 
action can be considered, categorized, evaluated, and selected or rejected. More generally a deliberative 
mechanism may be capable of counterfactual reasoning about the past and present and hypothetical 
reasoning about the future. The depth, precision, and validity of such reasoning can vary. Meta-
management processes add the ability to monitor, evaluate, and to some extent control processes occurring 
within the system in much the same way as the whole system observes and acts on the environment. The 
three layers operate concurrently and do not form a simple dominance hierarchy. Arrows represent flow of 
information and control, and boundaries need not be sharp in all implementations. 
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543 of 1456 DOCUMENTS 
Copyright 2003 The Financial Times Limited 
Financial Times (London,England) 
August 20, 2003 Wednesday 
London Edition 1 
SECTION: BACK PAGE - FIRST SECTION; Pg. 18 
LENGTH: 748 words 
HEADLINE: Wall Street spearheads push to secure academic freedom: A scheme that began in the 1930s, 
and helped physicist Felix Bloch and writer Thomas Mann, seeks a Dollars 10m revival. Gary Silverman 
reports 
BYLINE: By GARY SILVERMAN 
BODY: 
About a year and a half ago, a small circle of wealthy investors collected Dollars 2m (Pounds 1.2m) to 
conduct a novel experiment on the extent of global academic freedom. 
The group, which included =Soros, Henry Jarecki and Jeffrey Epstein, established a fund to 
help scholars escape threats in their home countries and find teaching work elsewhere. The donors made 
their offer in the spirit of the movie, Field of Dreams, which held that "if you build it, they will come". Still, 
they were stunned by the response. 
About 300 academics from 65 countries sought help from the Scholar Rescue Fund, which is being 
administered by the non-profit Institute of International Education. 
Many of the threats to scholars came from likely suspects - African warlords, Colombian drug 
traffickers, terrorists and religious fundamentalists. But the organisers were also struck by the heartbreaking 
singularity of so many of the cases. 
A marine biologist in a former republic of the Soviet Union angered government officials by studying 
local shellfish populations. An African academic was threatened after discovering that funds had been 
stolen from a university library. One western European government even sought help for a local scholar 
who was threatened by a separatist movement. 
"The overwhelming majority of cases involve people who haven't taken sides," said Allan Goodman, 
IIE president and chief executive. 
"They just happened to be scholars who are teaching in the wrong field, or they happened to be from 
the wrong ethnic group or, in one case, they have the same surname as the leader of a faction and they have 
been targeted." 
The extent of the problem led the organisers to a sad conclusion - their work needed to take a more 
permanent form. 
They are now trying to raise a Dollars 10m endowment for the Scholar Rescue Fund. They may also 
start an index of academic freedom that would spotlight abuses in particular countries. 
"The impact and need has been greater than we expected," says Mr Soros, comparing the effort to his 
work on behalf of central and eastern European dissidents in the 1980s. 
So far, the fund has helped 30 scholars from 19 countries escape persecution and find work at 
institutions ranging from Princeton University to the Geological Survey of Norway. The rescues 
themselves can be dangerous and the IIE often turns to human rights groups for logistical help. 
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( 
The fund arranges for the scholars to get teaching positions, providing annual stipends of up to Dollars 
20,000 to smooth the transition. 
The HE's role in helping intellectuals is not a new role as it started in the 1930s and was led by Edward 
R. Murrow, an HE assistant director and later a legendary CBS reporter. Among those it helped were Felix 
Bloch, the physicist. theologians Martin Buber and Paul 
Tillich, Thomas Mann, the novelist, and philosopher Herbert Marcuse. 
The latest effort to rescue scholars bears the imprint of Wall Street. Tom Russo, a Lehman Brothers 
,vice-chairman and an IIE trustee, has been a prime mover in the project. He helped recruit the donors and 
define the rationale for the rescue work. For Mr Russo, academic freedom is like market transparency - a 
"source of light" that keeps society functioning smoothly. 
Deciding on which requests should receive help has been a job worthy of Solomon. The fund has heard 
from scholars who live in dangerous places but face no particular threat as individuals - a requirement for 
receiving help. Mr Goodman says this is often the case in places such as Israel's occupied territories, 
although the IIE has made one rescue there. 
Dr Jarecki, a psychiatrist who made a fortune in bullion dealing and other ventures, said the fund is 
also trying hard to avoid contributing to a "brain drain" of academic talent in developing countries. Many of 
the applicants face threats to their security, but others simply want to move for economic reasons. 
However, the organisers say they are trying to resist the temptation of being too cautious in their work. 
He says he frequently brings up the example of a 1938 conference in Evian, France, that was held to 
discuss the resettlement of German and Austrian Jews. The Dominican Republic agreed to accept between 
50,000 and 100,000 Jews. But by the time the "proper" arrangements were made, a world war was raging 
and it was too late to do much good. 
In this case, Dr Jarecki says, the fund will work out how best to achieve its aims as it goes along. But, 
he adds: "I thought we should start by doing it." 
LOAD-DATE: August 19, 2003 
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Page 2 
581 of 1456 DOCUMENTS 
Copyright 2003 Associated Press 
All Rights Reserved 
The Associated Press State & Local Wire 
February 7, 2003, Friday, BC cycle 
SECTION: State and Regional 
LENGTH: 200 words 
HEADLINE: Financier pledges $30 million to support Harvard researcher 
DATELINE: CAMBRIDGE, Mass. 
BODY: 
Reclusive financier Jeffrey Epstein has pledged up to $30 million to Harvard University to support a 
newly recruited professor's research in the field of mathematical biology. 
A spokeswoman for Harvard president 
H. Summers confirmed Friday that Epstein's 
contribution will support the research of Martin A. Nowak, who is scheduled to join the Harvard faculty on 
July 1. 
Epstein, who reportedly manages billions of dollars from his private island in the Caribbean, already 
made a donation and plans to eventually establish a $30 million endowment to support Nowak's research, 
spokeswoman Lucie McNeil said. She did not specify how much he has already given. 
Nowak, 36, currently a professor at Princeton's Institute of Advanced Study, uses advanced 
mathematics to model human behavior and to study evolutionary theory, viruses and cancers. He was 
recruited to Harvard as part of Summers' commitment to grant tenure to young professors and those who do 
interdisciplinary research. 
The self-educated Epstein is both a longtime Harvard contributor and a benefactor of Nowak, to whom 
he previously donated $500,000, the Harvard Crimson student newspaper reported. 
LOAD-DATE: February 8, 2003 
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(26.) A. Sloman, "Architectural Requirements for Human-Like Agents both Natural and Artificial," K. 
Dautenhahn, Editor, Human Cognition and Social Agent Technology, John Benjamins, Amsterdam (2000), 
pp. 163-195. 
(27.) M. Minsky, "Common Sense-Based Interfaces," Communications of the ACM 43, No. 8, 67.73 
(2001). 
(28.) M. Minsky, "A Framework for Representing Knowledge," AI Laboratory Memo 306, Artificial 
Intelligence Laboralassachusetts Institute of Technology (1974), reprinted in The Psychology of 
Computer Vision, 
Winston, Editor, McGraw-Hill, New York (1975). 
(29.) D. Riecken, "An Architecture of Integrated Agents." Communications of the ACM 37, No. 7, 
107-116 (1994). 
Accepted for publication May 17, 2002. 
J. McCarthy 
Stanford University 
Stanford, California 
M. Minsky 
Massachusetts Institute of Technology 
Cambridge, Massachusetts 
A. Sloman 
University of Birmingham 
Birmingham, UK 
L. Gong 
IBM Research Division 
Hawthorne, New York 
T. Lau 
IBM Research Division 
Hawthorne, New York 
L. Morgenstern 
IBM Research Division 
Hawthorne, New York 
E. T. Mueller 
IBM Research Division 
Hawthorne, New York 
EFTA00176597

D. Riecken 
!BM Research Division 
Hawthorne, Ncw York 
M. Singh 
IBM Research Division 
Hawthorne, Ncw York 
P. Singh 
Massachusetts Institute of Technology 
Cambridge, Massachusetts 
IAC-CREATE-DATE: September 26, 2003 
LOAD-DATE: October 07, 2003 
EFTA00176598