Software Agents Go To The Movies
- By Dana Moore, Ed Greengrass
Long touted as a promising technology, software agents have seemingly vast potential
for electronic commerce. The possibilities, and the basic design considerations
for Internet Information Extraction (IE), can be illustrated by a look at a familiar,
simple and yet surprisingly rich domain: motion picture theaters, films and showings.
In fact, the newly emerging eXtensible Markup Language (XML) may be a useful
technology for enabling Internet IE agents targeted at all kinds of online listings.
The term "agent" has become a popular and exciting buzzword. Given the richness
and diversity of information sources, as well as the number of commercial opportunities
on the Internet, agents would seem to be especially valuable for Web interaction.
Yet agents that help users find their way intelligibly through this data maze
are comparatively rare.
What exactly is an agent? An agent is a program that a person or organization
(the "client") designates to perform a specified task. Once the agent knows
what its client wants it to do, it proceeds to carry out the task without the
need for constant human intervention. It can run unattended for long periods
of time (a week, a month, perhaps many months), until its task is done or its
client recalls it. It can visit and communicate with a variety of information
sources, and it can tailor its answer to a client's requirements.
To get a view on how agents, XML and the Web may work together in years to
come, it may be worthwhile to look at how a personal agent can help navigate
something many of us use on a regular basis: movie theater schedules. While
people can access movie information from different sources, dealing with existing
information sources and the servers that provide human access to these sources
can be difficult, time-consuming or practically impossible. Agents can bridge
the gap between what people want to ask, and what existing Web and phone systems
support. This gap exists not only in movies, but in almost any practical domain
- travel planning, stock trading and so on.
Information about movies and available movie showings is currently available
by phone, on the Web and, of course, in daily and weekend newspapers. But agent
technology allows for much more sophisticated searches. It also allows for interaction
between the movie-going public, movie producers and critics, and the theaters
that exhibit these movies.
So, what do people want to know about movies? The obvious questions are "What
movies are currently playing or will be playing next weekend?" and "Where and
when can I see my movie?".
In fact, there are many other questions. Perhaps what I would like to ask
is something like this:
- "Find the earliest showing of the ‘Blair Witch Project.'" (Or the latest
showing, or showings before noon, or showings within a specified time band).
- "I only want theaters with ample or handicapped parking spaces. Which are
- "What theaters are within a certain distance by road (or an estimated driving
time less than 30 minutes) from where I am right now?"
- "I like short movies. What's playing with a duration of less than two hours?"
- "Find only theaters with stadium seating and large screens."
- "I am looking for a multiplex showing two movies, one suitable for adults
and the other suitable for children under 10."
- "I'd like to see ‘Phantom Menace,' but only if there's handicapped parking
and stadium seating."
- "I can't recall either the title or the actors of a film I read about in
an intriguing review. I don't know if the film is still playing anywhere,
or whether it has moved to video. Also, I am not sure if the film will be
classified as foreign, art, comedy or drama. I remember that the film was
French, and can give a brief (two or three sentence) description of the story
line. What film is this? Where can I see it or rent it?"
But what if no movie showing satisfies all of a client's requirements? In that
case, the client may wish to indicate which criteria are absolute and which ones
may be relaxed; for example, the client may prefer a movie starting before noon,
but may accept a starting time up to 1:30 p.m. if all of the other criteria are
satisfied. Or the client may want to "weight" the criteria, indicating which ones
are more important; for example, handicapped parking and an early showing time
are twice as important as a large screen and stadium seating.
The customer may also want to specify all criteria in advance, and only be
notified if one or more showings satisfy all the absolute criteria. Or they
may prefer to be given a set of alternatives, none of which satisfy all the
criteria, but all of which "come close." Finally, the customer may want to know
one movie that satisfies their requirements, or a ranked list of movies that
satisfy or approximate their requirements.
All of the above requirements (except perhaps the last one) deal with actual
movie showings, and the theaters at which the movies are actually being exhibited.
But another possibility is a client who wants to study the larger set of movies
that are "coming" - movies that may or may not yet be scheduled. Indeed, it
may be the case that a decision has not yet been made whether a given movie
will be shown at all, whether it will go into general distribution or merely
be shown at an "art" theater in the region. By studying the set of forthcoming
movies, the customer may not only be able to determine which movies they are
likely to want to see, but cast a vote for bringing a desired film to the local
multiplex. A personal request to the manager of a local theater, or even to
the management of the chain to which the local theater belongs, may be ineffective.
But tens, hundreds and perhaps even thousands of votes on the Web may be quite
A personal agent can not only respond to a rich variety of requests, it can
respond to questions whose answers will not be available for a long time. An
agent can wait indefinitely - hours, days, weeks, months and so on - before
a desired condition is satisfied and it can alert a user. For example, a user
may want to be informed when a foreign film receiving little publicity is shown
in their viewing area in a theater with handicapped parking - a condition that
may not occur for months (if at all). If the film is never shown locally, the
user may then want to be informed when the film becomes available on video,
TV or on the Internet - an event that may not be advertised or even mentioned
in the local newspaper, or may be mentioned only on a day when they have not
read the paper. A human could easily miss these events, but a tireless agent
is unlikely to.
Agents: They remember
Agents differ from conventional information sources in another crucial respect:
They remember. Your movie agent may remember your previous preferences and tailor
its responses to your tastes. It may even make unsolicited suggestions based
on either your past requests or selections, or the selections of other users
who have tended in the past to exhibit similar tastes. (In principle, the "server"
that provides access to an information source could save the preferences or
"profiles" of all its clients. But it would not be very realistic for a server
to save the profiles of thousands of clients, especially if the profiles change
frequently. It would be even less realistic for thousands of Web servers to
each save the profiles of thousands of clients!)
An agent can interact with its customer to determine requirements and preferences.
It can then move to multiple Web server sites, and interact with each server.
Of course, a server can also interact directly with each client, as many Web
However, an agent can interact with a client at the client's local site to
determine requirements, then move to the site of an information source to interact
locally with its server. If the interactions are complex, this can greatly reduce
the amount of Internet traffic and speed up the interactions themselves.
More importantly, the interactive and logical capabilities of the agent can
exceed those of some or all of the servers with which it must interact. For
example, a user may specify a logical combination of criteria and constraints,
such as the examples given above. While the information source ("Moviefone,"
for example) may contain much of the needed information, its server may not
provide the user with the ability to ask such questions. Users must then ask
a series of questions and put the results together themselves. An agent, by
contrast, can issue a query to "Moviefone," evaluate the information according
to its client's requirements, issue another query to "Moviefone" on the basis
of this evaluation, and continue querying until the desired results are achieved.
Furthermore, a client's request may require information from a number of information
sources. With an agent, there is no need for a client to "surf" the Web and
piece the data into one logical whole. An agent can move from one information
site to another, accumulating the information its client needs. Moreover, the
calculation it performs at one site may determine which site it visits next;
for example, it may determine at a film producer site that Film A may be of
interest to its customer. It will then visit film magazine and film critic sites
to confirm that hypothesis, then visit a film distributor site to determine
if the film is available in the customer's area, and finally visit theater sites
to determine if showing times, parking and so on meet the customer's criteria.
Alternatively, the agent can generate "clones," or copies of itself, to visit
multiple sites simultaneously, speeding up the process of data collection and
logical calculation. The clones can then communicate with their parent to combine
their partial results into a final report for the client.
The capabilities described here do not require remarkably "intelligent" agents.
On the contrary, most of the required capabilities are already found in existing
software, such as database systems, information retrieval systems or data mining
Angle on XML
What is needed, and does not (in most cases) currently exist is for the pages
that exist on the Web, or that are created by information servers, to be "marked
up" in such a way that they describe their own content. For example, if a page
describes the dates and times of movies showing at a number of theaters, there
must be a standard way of saying (to external programs such as agents), "This
is the theater name, this is the movie name, this is the date at which the movie
is being shown at the given theater, this is the time of showing, etc." Moreover,
all of these items must be represented in standard ways; for example, is the
date represented as month/day/year, day/month/year or year/day/month?
In fact, XML already specifies the names of the data items contained in a
page, and how they are organized together. What is needed is for the members
of the XML community to agree on standards for representing generic types of
data such as names, dates and times. Then the participants in any given domain,
such as the film domain, could come together to build on these generic "data
types," agreeing on how to name, represent and organize the data specific to
their applications. This does not require "rocket science," only agreement on
what the customers want to know, and how to represent it.
Also, it should be noted that Web pages represented in a "markup" language
such as XML can support flexibility with respect to what information they provide.
XML can support flexibility with respect to what information agents search for.
One theater may advertise its ample parking and stadium seating. Another theater
not possessing those attributes can simply omit them from its Web page. An agent
looking for theaters with ample parking will not crash when it encounters a
site that does not mention parking. It may simply omit the given theater from
its list of candidates, or tell the customer that the theater has the desired
film at an ideal time, but comes up short on the criterion of parking. More
generally, different kinds of sites may provide different kinds of information;
for example, a theater site, a film producer site, a film critic site and a
generic movie site such as "Moviefone" may each provide quite different information
(and a different user interface). But the customer's agent can gather data from
all of these sites, provided that they each organize, name and represent their
data according to the common, agreed-upon conventions.
Finding sources of knowledge
Ultimately, one or more evaluation criteria operate in some ill-understood
mechanism so that a potential patron becomes aware of "content of interest."
Although emulating the logic that a human uses to express likes and dislikes
would be difficult for an automated personal agent, alerting a potential filmgoer
to possible films of interest is actually a solved agent activity. In one approach,
called collaborative filtering, a person fills out an online questionnaire,
and a profile is generated and persists in a closed system. Agents representing
a person's preferences can then look for other agents in the same system that
represent similar interests and ask them for recommendations. Another kind of
filtering, called content-based filtering, allows an agent to learn preferences
by asking the individual to rate material presented to them.
Software agent architectures must support processing strategies that enable
an agent to find and query knowledge sources, react to (and, where appropriate,
fuse) discovered knowledge and make decisions based on the knowledge it has
gained. Agents must therefore be either comprehensively knowledgeable in a given
domain (rather unlikely), know how to communicate with other agents that are
knowledgeable (still not likely), know how to glean information from HTML-like
documents in cyberspace (some promising results), or be able to locate sources
of structured knowledge (still in the future, but being worked on very hard).
An agent's first difficult task is finding domain-relevant knowledge resources.
The emergence of portals can be helpful (to humans at least) in a few domains,
but they are not sufficiently generalized. Search engines can be helpful (again,
to humans), but as with anything designed for browser consumption, the results
must be sieved through some mechanism that turns HTML source code into information
usable by an automated process.
Wrappers and meta-content
There is a surprising amount of regularity among Web pages that an agent can
exploit. For example, the agent could use a library of "wrapper" algorithms
to aid in information extraction from the pages in its itinerary. Wrappers map
non-standard page formats into a standard format for the benefit of visiting
agents. While wrappers are an interesting idea, they are not generally considered
feasible because they are site-specific, the solution does not scale to large
numbers of sites, sites may change their layout at will making wrappers invalid,
and agents cannot examine new sites for which no wrapper exists.
There are several ongoing research efforts to use AI methods for automatic
wrapper induction, and some results have been encouraging. However, if the page
is not regular or is extremely complex, the feasibility of induction drops significantly.
A far better approach would be for content providers to actually structure
the content of their Web pages for access by software other than visual browsers.
Indeed, much has been written about the significance of XML or XML-like strategies
for tailoring content to the end-user device. Markup languages are beginning
to appear to transform content both horizontally (for specific end-user devices)
and vertically (for specific applications or industries). Moreover, since XML
itself can act as a translator between XML representations, the implication
for application design is that it becomes much easier to tailor vertical content
for a wide range of devices - financial data (in Financial Product Markup Language,
FpML) for a speech output system (using Voice Output Markup Language, VxML.),
or movie data to a handheld device (using Handheld Device Markup Language, HDML).
One critical point for designers and developers to consider is that XML lends
some measure of syntactic rigor to content, but not necessarily semantic rigor.
XML allows us to dictate how entities like software agents can talk and exchange
information, but it does not validate the content of the conversation. Another
point is that more work is needed (and is being done in the XML community) to
refine document content description and validation beyond that offered in traditional
For example, movie sites might use a common XML description for show time
listings, but use a different separator character between hours and minutes.
Further, European sites would tend to express "1:40 p.m." as "13.40." Content
providers will ultimately be forced to adhere to unambiguous representation
standards, purely as a matter of competitive pressures. Moreover, the semantics
of each item must be standardized too; for example, is the show time in hours,
quarters of an hour, or hours and minutes? What functions are defined for the
given item; for example, comparison to a baseline time or comparison to a range
of times? On the other hand, some information sources are inherently unstructured,
while some are structured but variable. Agents will need to provide information
retrieval capability for unstructured data.
One design approach that could be used would be to create "schema oases,"
where agents might go to find out what sort of questions could be asked of a
given XML domain. The agent would return these questions to its home base and
construct an HTML form for the human user. A refinement of this strategy would
have both an XML DTD and style sheet appropriate to various devices. The ultimate
oasis site would also associate URL(s) for content providers that are willing
to respond to a visiting agent using the given XML schema. Although such a service
does not exist, the idea may be commercially viable.
In the scenario shown in Fig. 1 (p. 42), the agent would hold its intermediate
and final query results in XML form because it might not be certain what form
the output might need to take. It may be that certain activities might take
time to complete. Or the user might have commissioned the search from a Web
browser that is currently available by wireless. The agent might use a service
such as Portico, Roku or Activerse's Dingbot to find and connect to the end
user. In the worst case, the agent would E-mail its results back to its client.
Having considered some development implications of content and content structuring,
let us consider software agents as a design center. Customers can send out mobile
agents to survey this wealth of information. As depicted in Fig. 1, an agent
can act as a discovery tool, a data miner and an information integrator. An
agent can find films and showings satisfying a customer's needs or it can provide
alternatives approximating those needs. It can engage in a dialogue with the
customer, allowing them to modify their requirements based on what they learn.
Finally, it can persist and apply user preferences later.
Agents can also provide the flexibility to search according to a variety of
requirements - return only the first satisfactory film showing found, a list
of film showings satisfying the specified requirements - and interact with the
user to modify the requirements as necessary.
An agent can provide output to both a Web site - a vote or review - and to
the person it represents.
The increasing amount of information available to users on the Web, the growing
number of devices that can be used to access it, and the growth of e-commerce
challenge our current ways of thinking and will lead to new methods for Web
design. Using a familiar example of searching for movie listings on the Web,
we have considered some of the design challenges and techniques that are appropriate
to the problem of finding meaning in cyberspace.