ポリシ管理技術は自律的な分散システム管理を実現する手段として期待されている．実際のシステムへの本技術への適用を考えると，効率と安全性が重要な課題となる．そこで，本稿では，安全かつ効率的なポリシ処理を実現するフレームワークを提案する．全てのポリシを並行に処理するポリシ処理方法の場合，効率は良いが，ポリシ記述者は並行に処理されても安全なようにポリシを記述する必要があり，記述者に対する負担が大きい．一方，全てのポリシを逐次処理をするポリシ処理方法の場合，並行処理に伴う問題を考慮する必要がないため，ポリシ記述者の負担は軽減されるが，性能の点で問題がある．本稿で提案するフレームワークでは，同じ対象に対して副作用を持ったアクセスを行なう可能性のあるポリシの集合を検出し，そのようなポリシの集合に含まれる組合わせ以外の組み合わせとなるポリシの集合を並行に実行することで，効率と安全性の両方を実現する．

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The complexity of current software systems requires the ability to adapt at run-time, and the development of self-adaptive systems is one of the recent challenges for realizing dynamic adaptation. In this paper, we focus on the adaptation based on the components connection as a fundamental adaptation, and present our implementation framework for constructing self-adaptive systems on the basis of an agent platform. We reinforce the agent platform by adding some application program interface for behavior cooperation and its autonomic activation, and connect the behaviors with components in self-adaptive systems. We also introduce an implementation guideline: a way to identify the responsibilities for control loops and implementation patterns for these responsibilities. We also demonstrate the effectiveness of our framework and guideline through the results from our implementation experiments and show how they can be used to construct self-adaptive systems by using agent platforms.

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In agent-based simulation using actual social statistics, attributes of all the agents have to been decided to meet the statistics. However, they can’t be decided uniquely because statistics gives only limited number of aggregated values. To this problem, Hara et al. have proposed a method of formulating the problem as a constrained optimization problem, and solving it using simulated annealing. They applied it to estimation of future population. However, they didn’t carry out evaluation of computational performance of their model. In this paper, the authors improved implementation of their model to enhance computational performance, discuss convergence of the optimization process and evaluate computational load of their method. Estimation results are compared with an estimation using conventional cohort-component method.

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