TASK DEFINITION: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
PROBLEM: Potential games and decentralised partially observable MDPs (DecPOMDPs) are two commonly used models of multiagent interaction, for static optimisation and sequential decision making settings, respectively. In this paper we introduce filtered fictitious play for solving repeated potential games in which each player's observations of others' actions are perturbed by random noise, and use this algorithm to construct an online learning method for solving DecPOMDPs. Specifically, we prove that noise in observations prevents standard fictitious play from converging to Nash equilibrium in potential games, which also makes fictitious play impractical for solving DecPOMDPs. To combat this, we derive filtered fictitious play, and provide conditions under which it converges to a Nash equilibrium in potential games with noisy observations. We then use filtered fictitious play to construct a solver for DecPOMDPs, and demonstrate our new algorithm's performance in a box pushing problem. Our results show that we consistently outperform the stateoftheart Dec POMDP solver by an average of 100% across the range of noise in the observation function.

SOLUTION: Filtered Fictitious Play for Perturbed Observation Potential Games and Decentralised POMDPs

PROBLEM: Automated learning of patients' demographics can be seen as multilabel problem where a patient model is based on different race and gender groups. The resulting model can be further integrated into Privacy-Preserving Data Mining, where they can be used to assess risk of identification of different patient groups. Our project considers relations between diabetes and demographics of patients as a multi-labelled problem. Most research in this area has been done as binary classification, where the target class is finding if a person has diabetes or not. But very few, and maybe no work has been done in multi-labeled analysis of the demographics of patients who are likely to be diagnosed with diabetes. To identify such groups, we applied ensembles of several multilabel learning algorithms. The best performing multi label ensembles include BR/Hoeffding Tree, CC/Hoeffding Tree, BCC/Hoeffding Tree, BR/JRIP, CC/JRIP, BCC/ JRIP respectively. In the empirical part of this study, we used on the UCI Diabetics dataset of over 100,000 records, collected from 130 US hospitals. The dataset consisted of attributes that included personal demographics, diagnoses code, lab results, etc. Experiments conducted on datasets of 1000, 10000, 20000 samples, show that BR/JRip model achieves a high overall accuracy of 0.533 (1000 samples), 0.702 (10000 samples), 0.569 (20000 samples), improving over the baseline model ZeroR with accuracy of 0.526, 0.586, .562 respectively. Loss functions such as Rank Loss, One Error, Hamming Loss, and Zero One Loss are also low for BCC/JRIP model for all samples of dataset, making it the best candidate for better performance given the label dependencies.

SOLUTION: Multi-Labeled Classification of Demographic Attributes of Patients: a case study of diabetics patients

PROBLEM: Automation and computer intelligence to support<lb>complex human decisions becomes essential to manage large and<lb>distributed systems in the Cloud and IoT era. Understanding<lb>the root cause of an observed symptom in a complex system<lb>has been a major problem for decades. As industry dives into<lb>the IoT world and the amount of data generated per year<lb>grows at an amazing speed, an important question is how to<lb>find appropriate mechanisms to determine root causes that can<lb>handle huge amounts of data or may provide valuable feedback<lb>in real-time. While many survey papers aim at summarizing<lb>the landscape of techniques for modelling system behavior and<lb>infering the root cause of a problem based in the resulting<lb>models, none of those focuses on analyzing how the different<lb>techniques in the literature fit growing requirements in terms<lb>of performance and scalability. In this survey, we provide a<lb>review of root-cause analysis, focusing on these particular aspects.<lb>We also provide guidance to choose the best root-cause analysis<lb>strategy depending on the requirements of a particular system<lb>and application.

SOLUTION:
Survey on Models and Techniques for Root-Cause Analysis