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Agreements between Enterprises digitized by Smart Contracts in the Domain of Industry 4.0
The digital transformation of companies is expected to increase the digi...
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What are the Actual Flaws in Important Smart Contracts (and How Can We Find Them)?
We summarize and systematically categorize results from more than 20 sec...
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Adaptive Partition-based SDDP Algorithms for Multistage Stochastic Linear Programming
In this paper, we extend the adaptive partition-based approach for solvi...
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Psamathe: A DSL with Flows for Safe Blockchain Assets
Blockchains host smart contracts for crowdfunding, tokens, and many othe...
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The Complexity of Contracts
We initiate the study of computing (near-)optimal contracts in succinctl...
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Finding Macro-Actions with Disentangled Effects for Efficient Planning with the Goal-Count Heuristic
The difficulty of classical planning increases exponentially with search...
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A Clustering Approach to Solving Large Stochastic Matching Problems
In this work we focus on efficient heuristics for solving a class of stochastic planning problems that arise in a variety of business, investment, and industrial applications. The problem is best described in terms of future buy and sell contracts. By buying less reliable, but less expensive, buy (supply) contracts, a company or a trader can cover a position of more reliable and more expensive sell contracts. The goal is to maximize the expected net gain (profit) by constructing a dose to optimum portfolio out of the available buy and sell contracts. This stochastic planning problem can be formulated as a two-stage stochastic linear programming problem with recourse. However, this formalization leads to solutions that are exponential in the number of possible failure combinations. Thus, this approach is not feasible for large scale problems. In this work we investigate heuristic approximation techniques alleviating the efficiency problem. We primarily focus on the clustering approach and devise heuristics for finding clusterings leading to good approximations. We illustrate the quality and feasibility of the approach through experimental data.
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