Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Fundamental concepts of qualitative probabilistic networks
Artificial Intelligence
Qualitative reasoning: modeling and simulation with incomplete knowledge
Qualitative reasoning: modeling and simulation with incomplete knowledge
Mathematics and Computers in Simulation - Special issue: 3rd IMACS international workshop on qualitative reasoning and decision support systems
Decision Support Systems - Knowledge management support of decision making
Environmental Modelling & Software
Inference in qualitative probabilistic networks revisited
International Journal of Approximate Reasoning
Efficient reasoning in qualitative probabilistic networks
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Identifying independencies in causal graphs with feedback
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
ER '07 Tutorials, posters, panels and industrial contributions at the 26th international conference on Conceptual modeling - Volume 83
Environmental Modelling & Software
Position Paper: Modelling with stakeholders
Environmental Modelling & Software
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There is a dichotomy between advanced simulation models and flexible, simple tools for supporting policy-making. The former is difficult to use for policy-makers and the latter lacks in analytical value. It is a step forward to link these two types of tools in a way that enables the analytical value of the advanced models, while retaining the flexibility and comprehensibility of the simple tools. This paper presents a framework for such a linkage. The framework is based on an interactive cognitive mapping tool, which uses the qualitative probabilistic network (QPN) formalism to make qualitative (sign-based) calculations. This paper shows that there are several differences that need to be bridged. Each of these is discussed and approaches are presented. It is shown that (1) QPNs can be linked consistently to models with deterministic functions and continuous variables; (2) it is possible to deal with spatially and temporally explicit information; (3) despite the fact that QPNs must be a-cyclic, it is possible to capture feedback loops in a QPN-based tool. To prevent that negative feedback loops automatically result in ambiguous influences, we used a heuristic approach. The framework has been illustrated by analysing two models from literature with the QPN-based method.