Inference-Based Ambiguity Management in Decentralized Decision-Making: Decentralized Control of Discrete Event Systems
Inference-Based Ambiguity Management in Decentralized Decision-Making: Decentralized Control of Discrete Event Systems
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DOI：
10.1109/tac.2007.906158
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发表时间：
2005-12
影响因子：
6.8
通讯作者：
Ratnesh Kumar;S. Takai
中科院分区：
文献类型：
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作者：
Ratnesh Kumar;S. Takai
分散决策需要各个地方决策者之间的相互作用，以便作出全球性决策。每个局部站点有限的传感能力会在每个局部站点的决策过程中产生歧义。我们认为，这种模糊性有不同的等级。我们提出了一个去中心化决策的框架（特别适用于去中心化控制），它允许计算这种模糊度等级，并利用它们的知识来达到全局决策。每个局部决策都被标记为一定的模糊度等级或级别，其中零是最小的模糊度级别。全局决策被认为与局部决策相同，即具有最小模糊程度的决策。计算局部决策的模糊程度需要评估其自身的模糊性以及其他决策的模糊性，并在此基础上进行推理。对于分散的监督器的存在，使得对于每个可控事件，所有获胜的禁用或启用决策的模糊程度都被限定在某个数字N（这样的监督器称为N-推理），引入了N-推理可观察性的概念。我们证明了合容（C&P） V析容与反容（D&A）共可观测性与零推理可观测性相同，而条件C&P V D&A共可观测性与单位推理可观测性相同。我们还提供了高阶可观察推理语言的例子。我们的框架不需要将可控事件先验地划分为允许/反允许集，也不需要基于局部决策的合取/析取的全局控制计算，表明我们基于模糊性的方法更有效。
Decentralized decision-making requires the interaction of various local decision-makers in order to arrive at a global decision. Limited sensing capabilities at each local site can create ambiguities in a decision-making process at each local site. We argue that such ambiguities are of differing gradations. We propose a framework for decentralized decision-making (applied to decentralized control in particular) that allows computation of such ambiguity gradations and utilizes their knowledge in arriving at a global decision. Each local decision is tagged with a certain grade or level of ambiguity, with zero being the minimum ambiguity level. A global decision is taken to be the same as a ldquowinningrdquo local decision, i.e., one having the minimum level of ambiguity. The computation of an ambiguity level for a local decision requires an assessment of the self-ambiguities as well as the ambiguities of the others, and an inference based upon such knowledge. For the existence of a decentralized supervisor, so that for each controllable event the ambiguity levels of all winning disablement or enablement decisions are bounded by some number N (such a supervisor is termed N-inferring), the notion of N-inference observability is introduced. We show that the conjunctive-and-permissive (C&P) V disjunctive-and-antipermissive (D&A) co-observability is the same as the zero-inference observability, whereas the conditional C&P V D&A co-observability is the same as the unity-inference observability. We also present examples of higher order inference-observable languages. Our framework does not require the existence of any a priori partition of the controllable events into permissive/antipermissive sets, nor does it require a global control computation based on conjunction/disjunction of local decisions, exhibiting that our ambiguity-based approach is more efficient.