荒井 幸代

Semantic Web is a challenging framework to make Web information machine readable or understandable, but it seems not enough to make human's requirements for collecting and utilizing information automatically. The Agent technology becomes hopeful approach to bridge the gap between humans and machines. Agents may be autonomous and intelligent entities that may travel among agents and human. They get the requirements from human or other agents, and offer an appropriate solution through consulting among them. The main difference between agent and ordinary software development is the issue of coordination, cooperation and learning. This issue is very important for utilizing the web information. In this paper, we attempt to give an overview and research challenges with respect to the combination of machine learning and agent technologies with Semantic Web from the perspective of interaction as well as interoperability among agents and humans.

Current research issues on web services have come to center around flexible composition of existing services. Under the initiative of industry, flexible composition framework has been developed on a workflow model where flow of the processes and bindings among services should be known beforehand. In short, its framework realizes flexible composition within the available services that are not widely opened. This paper focuses on two limitations of current web service composition. One limitation is that it's hard to represent multi-agent scenarios consisting of several concurrent processes because it is based on a workflow model. The other limitation is that once composed, web service cannot be reused or transferrd for other requesters, because there is no function to put the semantics on composite web service. To overcome these limitations, we have developed scenario description language Q, which enables us to realize a web service composition reflecting a multi-agent's context not as a workflow but a scenario. Furthermore, we developed a system that translates multi-agent scenario to DAML-S, and that registers the translated DAML-S as a new Web service. We also discuss the availability of our system for designing an application to C-Commerce and Digital Cities.

The point we want to make in this paper is that Profit-sharing; a reinforcement learning approach is very appropriate to realize the adaptive behaviors in a multi-agent environment. We discuss the effectiveness of Profit-sharing theoretically and empirically within a Pursuit Game where there exist multiple preys and multiple hunters. In our context of this problem, hunters need to coordinate adaptively one another to capture all the preys, without sharing information, predefined organization and any prior knowledge around their environment. Pursuit Game itself is very simple but can be extended to a real problem. Our approach, Profit-sharing, is contrastive to other reinforcement learning approaches which are based on Dynamic Programming, such as Temporal Difference method and Q-learning, in that Profit-sharing guarantees convergence to a effective policy even in domains that do not obey the Markov property, if a task is episodic and a credit is assigned in an appropriate manner. Profit-sharing is also different from Q(1) and Sarsa(1) methods in that it does not need eligibility trace to manage the delayed reward. Though our monolithic implementation here seems to be an impractical in a real word, we need to discuss the validity of algorithm as a multiagent reinforcement learning context before introducing some structured frameworks into the monolithic method to extend its application. The contribution of this paper is that we introduce Profit-sharing as the effective algorithm in the multiagent domain and report its advantages and limitations without hierarchies.

This paper deals with planning actions of the cranes in a coilyard of steel manufacture. Each crane would be operated independently but it must share the rail and be required single-track operation among the other cranes. And complete information around the coilyard is not always available to each operator of the crane. Sometimes operator does not need whole information, but there exist a complicated interaction among the cranes. There are two main problems in this case. One is an allocating generated tasks to a certain crane and the other is a controlling cranes' execution to avoid collision. We focus the latter one in this paper and we approach to acquire the cooperative rules to evade collision among the cranes which might be very difficult to design by any experts. Instead of hand-coding these rules, we apply profit-sharing, a kind of a reinforcement learning method, in our multi-agent model. And show that the performance of cranes which are operated by reinforced rules is better than that of cranes modelling by the reactive planner using hand-coded rules.