By Su, Hartung, Girod
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Therefore, we use Q-learning as the learning algorithm used by our population of agents. We do not know of any other research that has attempted such massively concurrent learning by a large number of utility maximizing agents using single-agent reinforcement learning techniques where the agent utilities are closely coupled. Not only is the likelihood of convergence of such interlinked learning to eﬀective selections unclear a priori, no weak guarantees about performance can also be provided. Our experiments, however, show that independent Q-learning by concurrent learners with suﬃcient exploration is surprisingly robust in identifying most of the mutually beneﬁcial relationships in the society.
PONOMIR guarantees Pareto optimal agreements if the participating agents are cooperative-individually rational. The agreements reached also guarantees at least as much egalitarian social welfare as the agreements reached by the existing protocols. 8 Bidding for Bundles in Auctions Agents with preferences for bundles of items are faced with a diﬃcult computational problem when each of the several electronic auctions sell only one item. While an optimal bidding strategy is known when bidding for item bundles in sequential auctions, only suboptimal strategies are known for simultaneous auctions.
Goal of the Ontos solutions within the Ontos Semantic Portal is to semantically integrate the best and most often visited thematic Internet resources. All the above depicted services and components are discussed in the next sections of the paper. 2 Ontos Navigation Server (ONS) As it was mentioned above, main ONS modules and services are connected to semantic navigation and semantic ranking & relevance. 1 Semantic Navigation Semantic navigation allows the user to surf between the knowledge base objects through the relations, acquire information on attributes of these objects, and pass to the source documents.