Modeling time-varying uncertain situations using Dynamic Influence Nets

AbstractThis paper enhances the Timed Influence Nets (TIN) based formalism to model uncertainty in dynamic situations. The enhancements enable a system modeler to specify persistence and time-varying influences in a dynamic situation that the existing TIN fails to capture. The new class of models is named Dynamic Influence Nets (DIN). Both TIN and DIN provide an alternative easy-to-read and compact representation to several time-based probabilistic reasoning paradigms including Dynamic Bayesian Networks. The Influence Net (IN) based approach has its origin in the Discrete Event Systems modeling. The time delays on arcs and nodes represent the communication and processing delays, respectively, while the changes in the probability of an event at different time instants capture the uncertainty associated with the occurrence of the event over a period of time

Malaria intervention scale-up in Africa : effectiveness predictions for health programme planning tools, based on dynamic transmission modelling

Scale-up of malaria prevention and treatment needs to continue to further important gains made in the past decade, but national strategies and budget allocations are not always evidence-based. Statistical models were developed summarizing dynamically simulated relations between increases in coverage and intervention impact, to inform a malaria module in the Spectrum health programme planning tool.; The dynamic Plasmodium falciparum transmission model OpenMalaria was used to simulate health effects of scale-up of insecticide-treated net (ITN) usage, indoor residual spraying (IRS), management of uncomplicated malaria cases (CM) and seasonal malaria chemoprophylaxis (SMC) over a 10-year horizon, over a range of settings with stable endemic malaria. Generalized linear regression models (GLMs) were used to summarize determinants of impact across a range of sub-Sahara African settings.; Selected (best) GLMs explained 94-97 % of variation in simulated post-intervention parasite infection prevalence, 86-97 % of variation in case incidence (three age groups, three 3-year horizons), and 74-95 % of variation in malaria mortality. For any given effective population coverage, CM and ITNs were predicted to avert most prevalent infections, cases and deaths, with lower impacts for IRS, and impacts of SMC limited to young children reached. Proportional impacts were larger at lower endemicity, and (except for SMC) largest in low-endemic settings with little seasonality. Incremental health impacts for a given coverage increase started to diminish noticeably at above ~40 % coverage, while in high-endemic settings, CM and ITNs acted in synergy by lowering endemicity. Vector control and CM, by reducing endemicity and acquired immunity, entail a partial rebound in malaria mortality among people above 5 years of age from around 5-7 years following scale-up. SMC does not reduce endemicity, but slightly shifts malaria to older ages by reducing immunity in child cohorts reached.; Health improvements following malaria intervention scale-up vary with endemicity, seasonality, age and time. Statistical models can emulate epidemiological dynamics and inform strategic planning and target setting for malaria control

Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems

Development of robust dynamical systems and networks such as autonomous aircraft systems capable of accomplishing complex missions faces challenges due to the dynamically evolving uncertainties coming from model uncertainties, necessity to operate in a hostile cluttered urban environment, and the distributed and dynamic nature of the communication and computation resources. Model-based robust design is difficult because of the complexity of the hybrid dynamic models including continuous vehicle dynamics, the discrete models of computations and communications, and the size of the problem. We will overview recent advances in methodology and tools to model, analyze, and design robust autonomous aerospace systems operating in uncertain environment, with stress on efficient uncertainty quantification and robust design using the case studies of the mission including model-based target tracking and search, and trajectory planning in uncertain urban environment. To show that the methodology is generally applicable to uncertain dynamical systems, we will also show examples of application of the new methods to efficient uncertainty quantification of energy usage in buildings, and stability assessment of interconnected power networks

Using Influence Nets in Financial Informatics: A Case Study of Pakistan

The paper presents an application of Influence Nets (INs) in the field of financial informatics. Influence Nets have primarily been used in war games to model effects based operations but, as shown in this paper, they can prove to be equally useful in other domains requiring decision making under uncertain situations. The primary advantage of INs lies in their ability to acquire knowledge from subject matter experts in problem domains that rely heavily on experts’ opinion. A sample case study from the fields of economics and finance is presented in this paper. The case study models the choices faced by a developing country to recover her economy which is going through a difficult phase due to global financial crisis, internal law and order situation and political instability

Complexity reduction of influence nets using arc removal

© 2015 - IOS Press and the authors. The model building of Influence Nets, a special instance of Bayesian belief networks, is a time-consuming and labor-intensive task. No formal process exists that decision makers/system analyst, who are typically not familiar with the underlying theory and assumptions of belief networks, can use to build concise and easy-to-interpret models. In many cases, the developed model is extremely dense, that is, it has a very high link-to-node ratio. The complexity of a network makes the already intractable task of belief updating more difficult. The problem is further intensified in dynamic domains where the structure of the built model is repeated for multiple time-slices. It is, therefore, desirable to do a post-processing of the developed models and to remove arcs having a negligible influence on the variable(s) of interests. The paper applies sensitivity of arc analysis to identify arcs that can be removed from an Influence Net without having a significant impact on its inferencing capability. A metric is suggested to gauge changes in the joint distribution of variables before and after the arc removal process. The results are benchmarked against the KL divergence metric. An empirical study based on several real Influence Nets is conducted to test the performance of the sensitivity of arc analysis in reducing the model complexity of an Influence Net without causing a significant change in its joint probability distribution

Measurements, Models, Systems and Design

531 s.

ACon: A learning-based approach to deal with uncertainty in contextual requirements at runtime

Context: Runtime uncertainty such as unpredictable operational environment and failure of sensors that gather environmental data is a well-known challenge for adaptive systems. Objective: To execute requirements that depend on context correctly, the system needs up-to-date knowledge about the context relevant to such requirements. Techniques to cope with uncertainty in contextual requirements are currently underrepresented. In this paper we present ACon (Adaptation of Contextual requirements), a data-mining approach to deal with runtime uncertainty affecting contextual requirements. Method: ACon uses feedback loops to maintain up-to-date knowledge about contextual requirements based on current context information in which contextual requirements are valid at runtime. Upon detecting that contextual requirements are affected by runtime uncertainty, ACon analyses and mines contextual data, to (re-)operationalize context and therefore update the information about contextual requirements. Results: We evaluate ACon in an empirical study of an activity scheduling system used by a crew of 4 rowers in a wild and unpredictable environment using a complex monitoring infrastructure. Our study focused on evaluating the data mining part of ACon and analysed the sensor data collected onboard from 46 sensors and 90,748 measurements per sensor. Conclusion: ACon is an important step in dealing with uncertainty affecting contextual requirements at runtime while considering end-user interaction. ACon supports systems in analysing the environment to adapt contextual requirements and complements existing requirements monitoring approaches by keeping the requirements monitoring specification up-to-date. Consequently, it avoids manual analysis that is usually costly in today’s complex system environments.Peer ReviewedPostprint (author's final draft