In [KUOK92] Daniel Kuokka and Larry Harada describe an agent application whereby potential producers and consumers of information send messages describing their information capabilities and needs to an intermediary called a matchmaker. These descriptions are unified by the matchmaker to identify potential matches. Based on the matches, a variety of information brokering services are performed. Kuokka and Harada argue that matchmaking permits large numbers of dynamic consumers and providers, operating on rapidly-changing data, to share information more effectively than via traditional methods.
There are two distinct levels of communication with a matchmaker: the message type (sometimes called the speech act) and the content. The former denotes the intent of the message (e.g., query or assertion) while the latter denotes the information being exchanged (e.g., what information is being queried or asserted).
Since the content of requests and advertisements may not align perfectly, satisfying a request might involve aggregating or abstracting the information to produce an appropriate result. For example, if a source advertises information about automobiles while a consumer requests information about Fords, some knowledge and inference is required to deduce that a Ford is an automobile. Such transformation of data is an important capability, but its addition to a matchmaker must be carefully weighed. If knowledge about automobiles were added to a matchmaker, similar knowledge could be added about every other possible topic. Obviously, this would quickly lead to an impractically large matchmaker. Therefore, a matchmaker as such does not strictly contain any domain knowledge. However, a matchmaker is free to use other mediators and data sources in determining partners. Thus, it could farm out the automobile/Ford example to an automobile knowledge base to determine if a match exists.
Both matchmakers run as processes accepting and responding to advertisements and requests from other processes. Communication occurs via KQML, which defines specific message types (historically known as performatives) and semantics for advertising and requesting information. KQML message types include simple queries and assertions (e.g., ask, stream, and tell), routing and flow instructions (e.g., forward and broadcast), persistent queries (e.g., subscribe and monitor), and information brokering requests (e.g., advertise, recommend, recruit, and broker), which allow information consumers to ask a facilitator (Matchmaker) to find relevant information producers.
These two types of matchmakers were developed separately due to the differences between their content languages (logic vs. free text), and the resulting radical impact on the matching algorithms. They could, in principle, be integrated, but just as a matchmaker uses other agents for domain-specific inference, it is preferable to keep them separated, rather than creating one huge matchmaker. If desired, a single multi-language matchmaker may be implemented via a simple dispatching agent that farms out requests to the appropriate matchmaker. This approach allows many matchmakers, each created by researchers with specific technical expertise, to be specialised for specific classes of languages.
Experiments with matchmakers have shown matchmaking to be most useful in two different ways:
Yet, even though matchmaking has proven very useful in the above applications, several important shortcomings have been uncovered. Whereas queries can be expressed succinctly, expressing the content of a knowledge base (as in an advertisement) is a much harder problem. Current formal content languages are adequate for the simple examples shown above, but to go beyond advertising simple attributes quickly strains what can be represented. Additional research is required on ever more powerful content languages. The COINS matchmaker is, of course, not limited by representation. Here, the efficiency and efficacy of free-text matching becomes a limiting factor.
 See: Patil, Fikes, Patel-Schneider, McKay, Finin, Gruber, and Neches. The DARPA Knowledge Sharing Effort: Progress report. In Proceedings of the Third International Conference on Principles of Knowledge Representation and Reasoning. Morgan Kaufmann, 1992.
 As pointed out previously, one of the benefits of matchmaking is that it allows providers to take a more active role in information retrieval. Thus, just as requests can be viewed as an effort to locate an information provider, an advertisement can be viewed as an effort to locate a consumer's interests. This raises serious privacy considerations (imagine a consumer asking for a list of automobile dealerships only to be bombarded by sales offers from all of the dealerships). Fortunately, the various modes of matchmaking can include exchanges that preserve either party's anonymity.
 So, to "actively seek" does not mean that producers will be able to find out just exactly which users are looking for which information. In [KUOK92] it is explicitly stated that their matchmaker will never offer this "service" to producers. More than that, they will not even allow producers to find out what exactly other producers are offering (i.e. they are not allowed to view an entire description of what other producers are offering), nor are they able to find out which producers are also active as searchers of information (i.e. are both offering as well as asking certain information and/or services from the Matchmaker).
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