Matching Requests for Agent Services with Differentiated Vocabulary

To enable decentralized development of large societies of agents, agents should be able to selectively team with others based on declarative descriptions of services, rather than a priori knowledge. This capability is difficult to achieve because descriptions written by different developers may be terminologically heterogenous— including vocabulary from ontologies that are potentially inconsistent. For example, one agent might describe its service as (a formal equivalent of) “query planning for high-school biology”, while another agent wants to “find collections for advanced life sciences”. We want the latter agent to recognize that the former might satisfy its request. We have completed research on two aspects of this problem. Our Service Classifier Agent (SCA) supports selection of agent services in societies that are dynamic and evolving, but whose agents all use the same ontologies [Weinstein and Birmingham 97]. We have also developed an algorithm that identifies maximal similarity between concept definitions that are terminologically heterogenous [Weinstein 95]. The SCA uses description logic to maintain a subsumption taxonomy of available services. Agents define their services at runtime, using terms from a set of ontologies associated with the SCA (including the taxonomy of services). To find services, agents query the SCA. Queries describe the ideal service desired, but find the best available. If a new agent meets a request better than was previously possible, the requesting agent may automatically switch to using the new agent. The SCA thus facilitates evolution of the society to meet users' needs. Previously, ontologies used for agent communication have described the task domain, rather than agent services. To assess similarity despite terminological heterogeneity, we build rough mappings between source and target concepts. Mappings are sets of one-to-one correspondences between subgraphs in the source and target concepts. Of many possible mappings between a pair of concepts, the largest and most densely linked are evaluated as the best (these ideas come from research in analogy; see Owen [90] for a lucid overview).