Figure 1.
Institute for Intelligent
Systems
The University of Memphis
One important role for autonomous agents is to assume tasks previously
performed by humans. Such tasks often require communication with humans,
and the coordination of multiple activities. What sort of agent architecture
will empower an agent to collaborate with humans in the process of autonomously
carrying out an every day clerical task? What if this task requires the
coordination of several different activities, including the composition
of simple documents? Here we propose such an architecture and include a
preliminary report on its first implementation. This architecture enables
a softbot, Virtual Mattie, to actively gather information from humans, compose
announcements of next week's seminars, and mail them each week to a list
that she keeps updated, all without the supervision of a human. VMattie's
architecture combines Maes' behavior net architecture and Hofstadter and
Mitchell's Copycat architecture and significantly extends them. ``Living''
in a UNIX environment, VMattie's communication with humans is entirely via
email with no agreed upon protocol. Thus natural language processing in
a narrow domain is required. She also warns seminar organizers of impending
time or place conflicts. In future versions, VMattie will learn the habits
of organizers in order to more effectively dun them for information. With
her low level operations based entirely on codelets, VMattie can also be
viewed as a multiagent system. The implementation is in Java and Perl. VMattie
is also an interdisciplinary project, leaning heavily on a psychological
analysis of a corpus of the real Mattie's messages as a basis for natural
language understanding.
The task described here is to design, build, and experiment with an intelligent
autonomous clerical agent. Our agent will be a software agent "living"
in a UNIX system (Franklin and Graesser 1996). It will be intelligent, with
multiple high level perceptions and actions satisfying several drives, all
happening in a complex dynamic environment. Performing a task normally the
duty of a clerical worker, it will do so without any human intervention
whatsoever, making it autonomous. Email communications with humans using
natural language is required.
The high level design stage, resulting in a new control architecture, is
complete. As this is written we're in the midst of the low-level design
phase that leads to coding. By the camera-ready copy date, a prototype should
be up and running.
Clerical tasks requiring only email communication abound, for example some
payroll clerk's tasks, and some travel clerk's tasks (not travel agents).
Agents capable of automating such tasks should prove individually of small,
but significant value, by freeing a clerical person for other duties. Collectively,
they'll be immensely valuable by reducing personnel costs and improving
efficiency.
The particular demonstration task we've chosen is to produce an agent, Virtual
Mattie, that each week emails an announcement of the next week's seminars
scheduled in an academic department. VMattie will communicate with human
seminar organizers by email, compose seminar announcements, send them in
a timely fashion to a mailing list that she maintains. She'll also dun organizers
for their information when it's not forthcoming, and mail addenda and errata
as needed. In a latter incarnation, she'll also handle one-time colloquia.
Though these tasks aren't difficult for the real Mattie, it requires high-level
perception in the form of recognition and understanding in dealing with
free-form, typically incomplete, email messages from both organizers and
listees. Understanding requires inferencing. The task also requires keeping
track of dates and deadlines, of default seminar information, and of email
addresses of both organizers and listees. And, VMatiie must compose and
send email messages of several types such as announcements, addenda, errata,
reminders, and acknowledgments. These tasks are by no means trivial, given
the current state of our knowledge of software agents. Much should be learned
while solving the problems that will inevitably arise with human-VMattie
communication, and with implementing the underlying architecture.
Development of VMattie requires us to formulate, implement and observe a
new, high-level software agent architecture. This architecture leans on,
and significantly extends, two previous architectures, Hofstadter and Mitchell's
copycat architecture (1994, Mitchell 1993), and Maes' behavior networks
(1990, 1992) [For brief accounts of these architectures see Franklin 1995].
A Maes network, extended to deal with variables, with variable goals, and
with activation arising from internal states, provides informal planning
and action selection. The integrated copycat-like architecture composes
outgoing messages in a flexible and distributed manner. VMattie's narrow-domain
natural language understanding is pattern directed using another copycat-like
architecture, rather than a classic symbolic parser. We are experimenting
with building some emotions into the architecture (Bates, Loyall, and Reilly
1991, Sloman and Poli 1996), such as guilt at not getting an announcement
out on time, confusion at not understanding a message, and anxiety at not
knowing the speaker and title of an impending seminar. These replace the
single temperature variable in the original copycat architecture. VMattie,
in addition to being an autonomous agent, is very much a multiagent system
with all internal actions taken by internal agents (codelets in the terminology
of Hofstadter and Mitchell).
A high-level map of VMattie's architecture can be seen in Figure 1. The
three center modules, Goals, Behaviors and Attention Registers, constitute
a Maes behavior net with critical extensions. The Input Processing Knowledge
and Input Processing Workspace modules, together with their associated codelets,
implement an adaptation of a Copycat architecture, as do the Tracking Knowledge
Base and the Composition Workspace modules. All computations within VMattie's
architecture are carried out by codelets, as in the Copycat architecture.
Each behavior is implemented by a collection of codelets.
Figure 1.
VMattie has several distinct goals operating in parallel (goals in Maes'
terminology-drives might be more descriptive). VMattie wants:
1) to get the weekly seminar out in a timely fashion,
2) to maintain complete information on each of the ongoing seminars,
3) to keep her mailing list updated,
4) to acknowledge each incoming message.
These goals vary in urgency as email messages arrive and as the time for
the seminar announcement to be sent approaches. This variation in goal urgency,
other than on and off, is an enhancement to the behavior net architecture.
We are experimenting with goals being influenced by emotions, as mentioned
above. Goals provide activation to behaviors that fulfill them.
Behaviors are typically mid-level actions, many depending on several codelets
for their execution. Examples of behaviors might include 1) add-address-to-list,
2) associate-organizer-with-seminar, or compose-reminder (to organizer to
send speaker, title, etc.). The behavior net is composed of behaviors and
their various links, successor links, predecessor links, and conflictor
links (see Maes 1990, or Franklin 1995, 245-251 for a brief account). Our
behaviors must support variables. To associate-organizer-with-seminar immediately
asks which organizer and which seminar. VMattie's behaviors implement the
usual preconditions, action, add list and delete list allowing variables
in the contents of any of these. Picture an underlying digraph composed
of templates of behaviors and their links. Instantiated behaviors lie above
their templates. Real links are instantiated above their templates if the
values of their corresponding variables match. Activation spreads only through
instantiated links.
The attention registers hold information extracted from an incoming email
message. Acting like a structured blackboard, the attention registers make
this information available to codelets that need it. Each register holds
the content of a specified field. Fields include organizer-name, email-address,
date, speaker, seminar-name, etc. These field names label the behavior variables
discussed in the preceding paragraph. When occupied, attention registers
provide environmental activation to behaviors that can use their contents.
An important attention register, different from the others, is the type-of-message
register. Due to VMattie's narrow domain, there are only a small number
of message types. Examples include requests to delete a name from the mailing
list, and messages from organizers giving the title and speaker of a seminar
session.
The input processing knowledge module plays something of the role of the
slipnet in the Copycat architecture. It contains knowledge needed to understand
incoming messages. For example, it knows the various forms of "tuesday"
(tuesday, Tuesday, tues., Tues.), and the names of buildings that typically
house the seminars (Dunn Hall, Dunn, DH, Psychology, psychology, PSYC, Psyc,
psyc). This module differs from slipnet in that it's not associative.
The information to be stored in this module was obtained from careful analysis
of a corpus of messages sent to the real Mattie over a two year period.
The types of messages were identified, abbreviations noted, and usages surrounding
speaker, title and affiliation recorded. We also learned what sort of inferences
were needed due to only partial information being sent in the incoming messages.
Senders assume that Mattie will use default knowledge and context to decipher
otherwise cryptic messages. The analysis of this corpus also contributed
to the structure of the tracking knowledge base to be discussed later.
The input processing workspace corresponds to the Copycat architecture work
space, but is much more complex. This workspace hold the contents of an
incoming message while codelets work to understand it. Word or phrases deemed
significant, that is contents of a field, are first tentatively labeled
and later, if warranted, definitively marked. The input processing knowledge
module is regularly consulted. Inferences are made. The single most important
inference is to the type of message, which is the key to its understanding.
When understanding takes place, that is, each significant word or phrase
has been definitively labeled with a field name and the type of message
inferred with assurance, This information is transferred to the attention
registers by primitive codelets.
Primitive codelets are codelets that don't directly subserve any behavior,
but that independently perform housekeeping functions within the systems.
Some primitive codelets instantiate behaviors when their needed field values
appear in attention registers. Others poll for incoming messages and transfer
their contents to the input processing workspace.
The tracking knowledge base module contains information used in composing
outgoing messages. For example, it stores default information on ongoing
seminars, knowing that the Cognitive Science Seminar, organized by Art Graesser,
meets on Wednesdays at 1:30 pm in PSYC 425. This default information is
updated by behaviors. This module also tracks the current mailing list for
announcements. This mailing list is also updated by behaviors. Importantly,
this module also stores templates for the various types of outgoing messages.
As in the case of incoming messages, there are also only a small number
of types of outgoing messages. VMattie sends out reminders to organizers
asking for seminar information. She sends the weekly seminar announcement.
Errata and addenda are also sent. And she send an acknowledgment for each
incoming message. This acknowledgment contains VMattie's understanding of
the incoming message in a form something like:
"From your message of Friday, July 5th, I understand that Jeff Whitledge
of the University of Memphis is to speak to the Computer Science Seminar
on "Pandemat: A Pandemonium Based Artificial Life Agent" at 4:15
pm in Dunn Hall room 245."
The acknowledgment also explicitly invites correction.
Such a message, as every outgoing message, is composed in the composition
workspace. Composition consists of choosing a template and filling in the
fields appropriately. This is done by a simpler Copycat-like architecture,
with information coming from the knowledge tracking module and from the
attention registers. An ongoing copy of the current seminar announcement
template is always present in the composition workspace. This copy is updated
as new information arrives from the organizers. When the announcement is
mailed, a new copy containing only default information replaces it.
VMattie is written in Java and Perl. It takes advantage of experience gained
with a previous softbot, Sumpy (Song, Franklin and Negatu,1996)
The following quote is from Rodney Brooks of MIT:
"No one talks about replicating the full gamut of human intelligence
any more. Instead we see a retreat into specialized subproblems. Amongst
the dreamers still in the field of AI (...) there is a feeling that one
day all these pieces will all fall into place and we will see "truly"
intelligent systems emerge."
This assertion is no longer as true as when it was written (Davidsson 1996,
Johnson and Scanlon 1987, Moffat and Frijda 1995, Riegler 1994).
Virtual Mattie is the first project under the Cognitive Agent Architecture
and Theory (CAAT) mantel. Cognition typically includes short and long term
memory, categorizing and conceptualizing, reasoning, planning, problem solving,
learning, creativity, etc. An autonomous agent capable of many or even most
of these activities, including desires and perhaps emotions, will be referred
to as a cognitive agent. [Sloman calls such agents "complete."
(http://www.cs.bham.ac.uk/~axs/)]
Recently designed mechanisms for cognitive activities such as those mentioned
above include the Maes and Hofstadter and Mitchell architectures as well
as several others (Agre and Chapman 1987, Drescher 1991, Edelman 1989, Jackson
1987, Kanerva 1988, Wilson 1985, etc.). The CAAT strategy proposes to fuse
sets of these mechanisms to form control structures for cognitive software
agents. VMattie is the first exercise of this strategy.
Since the specification of every control architecture underlies some theory,
this strategy also entails the creation of theories of cognition. Conversely,
improvements in theory should lead to possible enhancements of the architecture.
This synergy between theory and architecture should lead to productive experimental
work in both artificial intelligence and cognitive science. VMattie is a
start in this direction.
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