An Agent Based System for Distributed Information Management: A Case Study
Sorry, I’m late :-(
Scholar Agent Alpha
Auction mechanism for authors (they pay for having their paper published). WHen they win the auction then they can use their «rights» to publish the paper. They are using a vickery auction (second price, closed envelope). the process is the following
- call for papers
- authors register themselves
- authors bids (once)
- system reports to winners and loser
- wining paper is submitted for publication (and paid)
More cycles can be done, so agents can modify their bids and try to win.
Conservative Re-Use Ensuring Matches for Service Selection
Automatic retrieval, a semantic is needed (OWL-S, WSMO) and IOPEs has to be annotated. And flexible matches are very important (services that do not exactly match). But current semantic matchmaking considers services as a single operation. She proposes for the services to have roles inside a composed service. But for many of the operations are offered by patterns that are still unkown. And context usually is not taken into account, so the global goal can not be preserved, so they define an action-based representation of the operations of a service and an extension of re-use ensuring matches to produce substitutions that preserve goals. A modal logic is used; states are fluents and there’re 4 types of operations: one-way, notification, request-response and solicit-response. With those elements, choreography is defined through roles and all complex services will still works if conservative substitution is guaranteed. ANd this can be done if casual chains of actions are not broken when actions are sbustituted. What is important is that the problem of determining if a substitution is conservative is decidible.
A big «but»: youi need to have a choreography. Services can not discover service compositions, but working over a composite service and look for suitable substitutions.
Evolving Ontological Knowledge Bases through Agent Collaboration
Problem: an agent gives a queary about a concept c that it is not known, so the question can not be answered. Option 1: consult a mediator. Option 2: consult other agents. After that, some fragment of the ontology can be requested so the original agent can augment its knowledge and perform better interactions. But it has a cost and may not benefit the agent (it do not weigh the cost) ==> evolutionary method to augment ontologies with concepts related to its intereset domain. Two steps:
- Merge Fragments: check which ones are related and merge them into one, create a powerset of all the axioms and, for each memmber of the powerset, check the consistence with existing ontology
- Selection Process: find the average depth of the fragment, select the related properties level by level and incorporate them to the ontology.
This has been checked with an small number of agents (5 query and 10 speciliased) and several benchmarks. But htere’s no method for measuring the complexity of a T-Box.
Very interesting. One of the best papers I’ve seen these days.