Probabilistic modeling and reasoning in multiagent decision systems

Ph.DDOCTOR OF PHILOSOPH

Assessment and Diagnosis of Human Colorectal and Ovarian Cancer using Optical Imaging and Computer-aided Diagnosis

Tissue optical scattering has recently emerged as an important diagnosis parameter associated with early tumor development and progression. To characterize the differences between benign and malignant colorectal tissues, we have created an automated optical scattering coefficient mapping algorithm using an optical coherence tomography (OCT) system. A novel feature called the angular spectrum index quantifies the scattering coefficient distribution. In addition to scattering, subsurface morphological changes are also associated with the development of colorectal cancer. We have observed a specific mucosa structure indicating normal human colorectal tissue, and have developed a real-time pattern recognition neural network to localize this specific structure in OCT images, enabling classification of the morphological changes associated with the progression of human colon cancer. Differentiating normal from malignant tissues is critically important, however, identifying different subtypes of abnormalities is also useful in clinical diagnosis. We have designed a feature extraction method using texture features and computer-vision related features to characterize different types of colorectal tissues. We first ranked these features according to their importance, then trained two classifiers: one for normal vs. abnormal, and the other one for cancer vs. polyp, where polyp is a pre-cancer marker. In assessing tissue abnormalities, optical absorption reveals contrast related to tumor microvasculature and tumor angiogenesis. Spatial frequency domain imaging (SFDI), a powerful wide field, label-free imaging modality, is sensitive to both absorption and scattering. We designed a computer-aided diagnostic algorithm, AdaBoost, to use multispectral SFDI imaging for ex vivo assessment of different types of colorectal tissues, including normal and cancerous tissue and adenomatous polyps. For diagnosis of human ovarian cancer, we first designed a histogram-based feature extraction algorithm. Then we trained and tested traditional machine learning methods utilizing these histogram features for ovarian cancer diagnosis. We also explored the use of these features in characterizing human fallopian tubes, which are believed to be the origin of the most lethal subtype of human ovarian cancers

Iterative Online Planning in Multiagent Settings with Limited Model Spaces and PAC Guarantees

Methods for planning in multiagent settings often model other agents ’ possible behaviors. However, the space of these models – whether these are policy trees, finite-state controllers or inten-tional models – is very large and thus arbitrarily bounded. This may exclude the true model or the optimal model. In this paper, we present a novel iterative algorithm for online planning that consid-ers a limited model space, updates it dynamically using data from interactions, and provides a provable and probabilistic bound on the approximation error. We ground this approach in the context of graphical models for planning in partially observable multiagent settings – interactive dynamic influence diagrams. We empirically demonstrate that the limited model space facilitates fast solutions and that the true model often enters the limited model space

Approximating Value Equivalence in Interactive Dynamic Influence Diagrams Using Behavioral Coverage

Interactive dynamic influence diagrams (I-DIDs) provide an explicit way of modeling how a subject agent solves decision making problems in the presence of other agents in a common setting. To optimize its decisions, the subject agent needs to predict the other agents' behavior, that is generally obtained by solving their candidate models. This becomes extremely difficult since the model space may be rather large, and grows when the other agents act and observe over the time. A recent proposal for solving I-DIDs lies in a concept of value equivalence (VE) that shows potential advances on significantly reducing the model space. In this paper, we establish a principled framework to implement the VE techniques and propose an approximate method to compute VE of candidate models. The development offers ample opportunity of exploiting VE to further improve the scalability of I-DID solutions. We theoretically analyze properties of the approximate techniques and show empirical results in multiple problem domains

Team behavior in interactive dynamic influence diagrams with applications to ad hoc teams

Planning for ad hoc teamwork is challenging because it involves agents collaborating without any prior coordination or communication. The focus is on principled methods for a single agent to cooperate with others. This motivates investigating the ad hoc teamwork problem in the context of individual decision making frameworks. However, individual decision making in multiagent settings faces the task of having to reason about other agents' actions, which in turn involves reasoning about others. An established approximation that operationalizes this approach is to bound the infinite nesting from below by introducing level 0 models. We show that a consequence of the finitely-nested modeling is that we may not obtain optimal team solutions in cooperative settings. We address this limitation by including models at level 0 whose solutions involve learning. We demonstrate that the learning integrated into planning in the context of interactive dynamic influence diagrams facilitates optimal team behavior, and is applicable to ad hoc teamwork.Comment: 8 pages, Appeared in the MSDM Workshop at AAMAS 2014, Extended Abstract version appeared at AAMAS 2014, Franc

Team Composition in PES2018 using Submodular Function Optimization

With the development of computer game technologies, gameplay becomes very realistic in many sports games, therefore providing appealing play experience to game players. To get the victory in a football pitch, the team composition is pretty important. There is little research on the automatic team composition in sports games particularly in a popular game of Pro Evolution Soccer (PES). In this paper, we consider the team composition as one team player recommendation problem since a team is composed of several players in a game. Subsequently, we aim to recommend a list of sufficiently good football players to game players. We convert the team player recommendation into one optimization problem and resort to the greedy algorithm-based solutions. We propose a coverage function that quantifies the degree of soccer skills to be covered by the selected players. In addition, we prove the submodularity of the coverage function and improve a greedy algorithm to solve the function optimization problem. We demonstrate the performance of our techniques in PES2018.</p