SimRisk: An Integrated Open-Source Tool for Agent-Based ...

2. Proof extraction and game-based result interpretation. Probabilistic model checking analyzes
a system on a stochastic property by constructing a mathematical proof. Such proof carries
rich information about the system with respect to the stochastic property. For instance, in
context of supply-chain analysis, a proof can be used to understand analysis result and to
debug a supply-chain design. Furthermore, probabilistic model checkers like PRISM [Hinton
et al., 2006] answers the maximal (or minimal) probability in which a stochastic property may
hold on a supply chain. A proof can be used to formulate an optimal solution that reaches
such maximal (or minimal) probability.
Current research on extracting proofs from probabilistic model checkers is still limited because
of the complexity of these proofs. Existing work (cf. [Han et al., 2009]) emphasized on
formats of proofs. More research is needed on how to extract these proofs from existing
model checking algorithms and interpreting them to end users. In this project, we will study
the problems of proof extraction and result interpretation in context of supply chain analysis.
We will extend Tan and Cleaveland [2002]’s previous works on extracting proofs from a
traditional model checker to the domain of probabilistic model checking. Previously Tan
[2002] also developed a generic game-theoretic framework for interpreting proofs from various
automated verification procedures. The framework worked by playing an interactive game
with a user. By making decisions during the game, the user could choose part of the proof
(s)he wanted to explore. Game-theoretic approach is especially suitable for explaining proofs
with branching logics, including proofs from formal stochastic analysis of supply chains. Such
proofs are (potentially infinite) decision trees extended with probabilities and rewards. In
this project we will develop a game-theoretic approach that allows an end user to explore
proofs constructed by a probabilistic model checker.
5.4 Specific aim 4: implement an open source tool for knowledge dissemination
and technology transfer
The purpose of this research is to enable modeling and automated stochastic analysis technology
that are efficient and scalable for real-world large-scale supply chains. Besides the project’s proposed
technological advances, the success of the project also largely depends on how effective we can
disseminate knowledge and transfer technology to other researchers and practitioners. An opensource
tool will be an excellent vehicle for this purpose: it allow practitioners to try out new
technology and integrate the new tool to his/her existing workflow. It also provides an open
platform for researchers to test and extend new technology.
Our team members have successfully developed a variety of open-source tools including Concurrent
Workbench [Cleaveland et al., 2000], M 2 IST [Tan, 2006], and most recently Simrisk [Tan
and Xu, 2009b]. We will apply these experience and skills to this project. Specifically we will build
the tool on Eclipse [the Eclipse Foundation, since 2004]. Eclipse is a popular open-source software
development platform. Its model-based design frameworks EMF and GMF reduce the time and
the cost for implementing an agent-based modeling framework. Eclipse supports several code generation
frameworks including JET and Acceleo that can be used for building the reconfigurable
generative simulation engine. Finally Eclipse adopts an open architecture. Other researchers can
integrate their own analysis algorithms to the tool.
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