A technology resource article by Mary Harrsch © 2005
College of Education University
of Oregon [2005]
My virtual professor may not be as sophisticated as agents that have been developed to pass the Turing Test but I hope I have provided a framework for the development of a rigorous inquiry-based learning system.
Wilson , M. "Metaphor to Personality: The Role
of Animation in Intelligent Interface Agents." Animated Interface
Agents: Making them Intelligent in
conjunction with International Joint Conference on Autonomous Agents. Nagoya , Japan
Oxford ,
UK
For those of you interested in artificial intelligence development, here is an archive copy of a presentation I gave in 2005 (I'm consolidating my online contributions!)
Extending the Learning
Environment:
Virtual Professors in Education
By Mary Harrsch
Network & Management Information Systems
Six years
ago [1999], my sister was telling me about a fascinating History Alive Chautauqua event she
had attended near Hutchinson ,
Kansas
My quest
led me to a website that included links to a number of different
software agent projects. I learned that
the type of agent I needed was commonly called a “chatterbot”. The first “chatterbot” was actually developed
long before the personal computer. In
the early 1960s, Joseph Weizenbaum created “Eliza”,
a virtual psychoanalyst.
In his
efforts to create a natural language agent, Weizenbaum pointed out that he had to
address the technical issues of:
- the
identification of key words,
- the
discovery of minimal context,
- generation of responses in the absence of
keywords
As I
began to explore different agent implementations, I found that, in addition to
these issues, the application needed to be able to prioritize keywords to
discern the most appropriate response.
Several agents I evaluated, including Sylvie, a desktop assistant, developed by Dr. Michael("Fuzzy") Mauldin, Artificial Life’s Web Guide , Carabot
500 developed by U.K. company, Colorzone, and Kiwilogic’s Linguibot (now Artificial Solutions, Inc.), used slightly different
methods to set the priority of subject keywords to select the most appropriate
responses. The response with matching
keywords under the subject with the highest level setting was “fired” –
displayed to the user. However, when
editing their script files, I found keeping track of subject priorities was
challenging.
Another
problem with many script-driven agents I evaluated was the use of left-to-right
parsing sequences that did not compensate for a variance in the order of
keywords in a question. Each query had to be evaluated for subject and for
matching character strings, based on left-to-right word order with the use of
various “wildcard” characters to indicate placement of keywords within the
overall question. Therefore, you often
had to have multiple script entries to compensate for different word
order. For example, if a student asks
“How do I change my password in e-mail?” you would need one script entry. If
the student asks “How do I change my e-mail password?” a different script entry
would be required:
* email
* * password * as well as
*
password * * email * to trap for either wording.
Although
this attention to script design resulted in improved response accuracy the
scripting knowledge required for these agents was not something I would expect
a faculty member to have the time or desire to learn.
A third
problem with several of the agent applications I used was the necessity to unload
and reload the agent each time the script file was edited. If students were actively querying the agent,
you could not save any script changes until the script file was no longer in
use.
When I
invested in the Enterprise Edition of Artificial Life’s WebGuide software, I
also realized the importance of a logging feature that I could use to study and
improve my guide’s responses. I recognized
the importance in a virtual tutoring environment of having the ability for a
student to print out a transcript of their tutoring session for future study. Not only was this feature absent in the
agents I evaluated, but the responses produced using Javascript or Flash would
not allow the user to highlight and copy responses to the clipboard either.
One day,
I explored UltraHal Representative, developed
by Zabaware, Inc. I liked the ability Ultrahal provided to program the agent through a web
interface. It had the capability to
include links to related information. It
could be customized with personalized graphics.
It logged interactions. Best of
all, it had a straightforward approach to editing - no scripting – just type
your question three different ways then type your intended response.
But, I
soon discovered that without the ability to identify keyword priority, I found that
the results using whatever algorithm was built into the agent engine were too
inaccurate for a virtual tutoring application.
I needed
a product that could be programmed to be “omniscient”.
“Effective
ITS require virtual omniscience -- a relatively complete mastery of the subject
area they are to tutor, including an understanding of likely student
misconceptions.” (McArthur, Lewis, and Bishay, 1993)
I needed
a virtual professor that could be “programmed” by real professors, the
individuals who would have a mastery of the subject and an understanding of
student misconceptions. But all of the chatterbots I had encountered, so far
(with the exception of Ultra Hal), required knowledge of scripting that most
faculty members do not have the time
to learn. I would not have the time to
provide one-on-one time with faculty developers and paying a programmer to work
with a faculty member is also too expensive. (I noticed most developers of commercial
agents actually relied on the scripting needs of their clients for their
primary revenue stream.) So, I decided
to attempt a radically different approach to agent design.
I am an
experienced Filemaker Pro solutions developer and one day I was reviewing some
of Filemaker’s text functions and realized that the position function could be
used to detect key words in a text string. The beauty of the position function is that
the keyword can be identified anywhere within the target text. It is not dependent on a left to right
orientation. Filemaker is also not case
sensitive. Also, Filemaker Pro allows
most scripting calls for text processing to be used with their Instant Web
Publishing interface. I realized this would greatly simplify web integration.
So, reviewing
my experiences with the agent applications I had used, I developed a list of
features that I wanted to incorporate:
Web
functionality:
Multiple
agents controlled by a single administration console
Web-based
query interface
Web-based
editing interface
Multiple
graphic format support
Web
accessible logging function for both agent editor and student user
Ability
to display related resources
Query
processing functionality:
Question
context awareness (who, what, when, where, why, how, etc)
Ability
to weight keywords by importance without user scripting
Ability
to return an alternate response if a question is asked more than once
Ability
to use one response for different questions
Ability
to process synonyms, international spelling differences, and misspellings
Independent
of word order
Not case
sensitive
Structural
Design:
Modular
design to enable importation of knowledge modules developed by others
Agent
specific attributes to customize the interface and responses such as a personal
greeting, the opportunity to use the person’s homepage as a default URL,
information about area of expertise and research interests for alternative
agent selection criteria, custom visual representations, etc.
I began by designing my response
search criteria. I programmed the agent search
routine to categorize responses by the first word of the query – usually What,
Where, Why, How, Who, Did, Can, etc. to establish the question
context. Then I used position formulas to test for the presence of keywords. I then developed an algorithm that weighted
the primary keyword or synonym and totaled the number of keywords found in each
record.
I designed the search function so
that when the visitor presses the button to ask their question, the database
first finds all responses for the question category (who, what, when, etc.) containing
the primary keyword (or its synonym). Responses
are then sorted in descending order by the total sum of keywords present in
each response. The first record – the one with the most keyword matches
– is displayed as the answer.
If there are no category
responses containing the primary keyword, then a second find will execute to
look for all responses with the keyword regardless of category. In working with other agent products, I have
found that if you return a response with at least some information about the
keyword, even if it is not an exact answer to the question, the student assumes
the agent recognized their question and may learn auxiliary information that is
still helpful to them.
For example, if a visitor asks my
virtual Julius Caesar if he really loved Cleopatra, he will answer
“Cleopatra…ah, what an intriguing woman.”
Not only is this more in character with Caesar
(most of his female dalliances were for political reasons) but the answer could
also be appropriate for a different question, “What did you think of
Cleopatra?” My search routine would find
it in either case because of the weighting of the primary keyword, Cleopatra.
If there are no responses
containing the primary keyword, a third find looks for any generic category responses. For example, if a student asks who someone is
and you have not programmed your agent with a specific answer for the keyword
(the person they are asking about), the agent will reply with an appropriate
“who” response such as “I’m afraid I’ve never made their acquaintance.”
If a student’s question does not
begin with any words set as category words, the last find will return a generic
“what” response such as “I may be a fountain of knowledge, but I can’t be
expected to know everything.” Programming
the agent with default generic responses, ensures that the agent always has
something to say, even if it knows nothing about the subject. I developed
a small database of generic responses for each question category that is
imported into an agent database each time a new agent is created. The faculty member can go through the
responses and edit them if they wish.
Next, I turned my attention to
the faculty’s content editing interface.
I wanted the faculty member to enter a proposed question, designate a
primary keyword and synonym, supply any other keywords they thought were
important to identify more precisely the desired response, and the desired
response.
I also provided a button that
enables a faculty member to quickly generate a different answer for the same
question or a different question for the same response.
I created a field that is
populated with a different random integer on each search. By subsorting responses by this random
integer, it enables the agent to offer a different response to the same
question if the question is asked more than once. This supports the illusion of the agent being
a “real” person because it will not necessar ily
return the same identical response each time.
“Believable agents must be reactive and robust, and their behaviors must
decay gracefully. They must also be variable, avoiding repetitive actions even
when such actions would be appropriate for a rational agent. They must exhibit
emotion and personality. Ultimately they must be able to interact with users
over extended periods of time and in complex environments while rarely
exhibiting implausible behaviors.” – Dr. Patrick Doyle, Believability
through Context: Using “knowledge in the world” to create intelligent
characters
With the “engine” of my agent
developed, I turned my attention to the visual representation of the character. In their paper, The Relationship Between Visual Abstraction
and the Effectiveness of a Pedagogical Character-Agent, Hanadi Haddad and Jane Klobas of Curtin University of Technology, Perth , Western
Australia
“Wilson
Unlike detailed realistic
drawings, sketches help focus the mind on what is important, leaving out or
vaguely hinting at other aspects. Sketches promote the participation of the
viewer. People give more, and more relevant, comments when they are presented a
sketch than when they are given a detailed drawing. A realistic drawing or
rendering looks too finished and draws attention to details rather then the
conceptual whole (Stappers et al, 2000).
“On the other hand,
research by psychologists suggests that people may put considerable cognitive
effort into processing abstract representations of faces (Bruce et al. 1992;
Hay & Young 1982). It is possible, therefore, that response to
anthropomorphised character-agents, and especially their faces, may differ from
responses to sketches. Gregory and his colleagues (1995) conducted studies on
human response to faces at the physiological level. They demonstrated that
humans are particularly receptive to faces. In terms of recognition,
participants in their studies were more responsive to real faces than to
abstracted line faces. They speculated, however, that people spend longer
studying abstracted line faces and may find them more interesting (Gregory et
al. 1995). If this is so, then contrary to theories of information design, an
abstract face may introduce more distraction into the communication than a
realistic face.”
Filemaker Pro provides
multimedia container fields that enable me to include still images, animations,
or even video clips. However, not only
is creating a unique graphic for each response time consuming, motion video
files can be quite large and slow down the delivery of response information
over the web. Working with other agents,
I had noticed that just the slight eye movement of a blink can be enough to
reinforce the illusion of a sense of presence. This approach straddles the two
opposing theories described above. I
would utilize a real face to capitalize on the human receptivity to a real face
but keep animation to a minimum to reduce distraction. I also think the use of a real faculty
person’s face serves to reinforce the bond between the instructor and the
student. A blink is also very easy to create from any faculty portrait.
I use an inexpensive animation
tool called Animagic GIF Animator. I begin with a portrait of the faculty
member. I open it in Photoshop (any
image editor would suffice) and, after sampling the color of the skin above the
eye, I paint over the open eye. Then I
open an unedited copy of the portrait in Animagic, insert a frame and select
the edited version of the portrait. I
then set the open eye frame to repeat about 250 times and the closed eye frame
to repeat once. Then loop the animation.
I created a related table that
stores all unique information about each agent including their default image,
their default greeting, their login password, their area of expertise, their
email address and their homepage URL. I also developed a
collection of alternate avatars to use for agent images in case some faculty
were camera-shy. These were created with
Poser
using their ethnic character library.
Finally, I designed the login
screen where the student selects the tutor to whom they wish to converse. Upon selecting the tutor and pressing the button “Begin
Conversation”, the student is presented with the query screen including the
individual greeting for the tutor selected.
I also provided a button for the
faculty to use to login to edit their agent.
It takes them to a layout that prompts them for a name a password.
Famed World War II cryptographer, Alan Turing, held that
computers would, in time, be programmed to acquire abilities rivaling human
intelligence.
 |
| Alan Turing at age 16. |
“As part of his argument Turing put forward the idea of an 'imitation game',
in which a human being and a computer would be interrogated under conditions
where the interrogator would not know which was which, the communication being
entirely by textual messages. Turing argued that if the interrogator could not
distinguish them by questioning, then it would be unreasonable not to call the
computer intelligent.” – The Alan Turing
Internet Scrapbook
My virtual professor may not be as sophisticated as agents that have been developed to pass the Turing Test but I hope I have provided a framework for the development of a rigorous inquiry-based learning system.
“Effective inquiry is more than just asking questions. A
complex process is involved when individuals attempt to convert information and
data into useful knowledge. Useful application of inquiry learning involves
several factors: a context for questions, a framework for questions, a focus
for questions, and different levels of questions. Well-designed inquiry
learning produces knowledge formation that can be widely applied.” - Thirteen Ed
Online.
References:
McArthur, David, Matthew Lewis,
and Miriam Bishay. "The Roles of Artificial Intelligence in Education:
Current Progress and Future Prospects".
1993. Rand .
<http://www.rand.org/education/mcarthur/Papers/role.html#anatomy >.
Doyle, Patrick.
"Believability through Context Using "Knowledge in the World" to
Create Intelligent Characters." Trans. SIGART: ACM Special Interest Group
on Artificial Intelligence. International Conference on Autonomous Agents.
Session 2C ed. Bologna , Italy New
York , NY , USA
Haddad, Hanadi, and Jane Klobas.
"The Relationship between Visual Abstraction and the Effectiveness of a
Pedagogical Character-Agent." The First International Joint Conference
on Autonomous Agents & Multi-Agent Systems. Bologna , Italy
Stappers, P., Keller, I. & Hoeben, A.
2000, ‘Aesthetics, interaction, and usability in
‘sketchy’ design tools’, Exchange Online
Journal, issue 1, December, [Online],
[2004, August 3].
Bruce, V., Cowey, A., Ellis, A. W. &
Perrett, D. L. 1992, Processing the Facial Image.
Hay, D.C., Young, A.W. 1982, ‘The human
face’, in Normality and Patholgy in
Cognitive Function, Ellis, A.W. ed., London , Academic Press, pp. 173-202.
Gregory, R., Harris, J., Heard, P. &
Rose, D. (eds) 1995, The Artful Eye, Oxford
University Press,Oxford .
"Thirteen Ed Online:
Concept to Classroom". 2004. Educational Broadcasting Corporation. 8/9/04 2004. <http://www.thirteen.org/edonline/concept2class/
>.
Hodges, Dr. Andrew. "The
Alan Turing Internet Scrapbook". Oxford , 2004. (3/15/2004 ): University of Oxford 8/09/04 2004.
<http://www.turing.org.uk/turing/scrapbook/test.html >.
 
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