Planning the motion of bearings-only sensors is critical for enabling accurate tracking of the positions of moving targets. In this paper, we demonstrate planning the observer's motion over horizons greater than one step for estimating an unknown and varying number of indistinguishable, maneuvering targets of interest using a probability hypothesis density (PHD) filter, with a Rériyi divergence reward for selecting actions. We describe approximations to make this approach computationally feasible, and we propose using Monte Carlo tree search (MCTS) to further reduce the cost. Finally, we present simulation results showing that longer planning horizons reduce the error in the estimates and that MCTS can reduce the cost of planning without sacrificing the quality of the estimates.

BACKGROUND: Recent clinical advances in cancer immuno-therapeutics underscore the need for improved understanding of the complex relationship between cancer and the multiple, multi-functional, inter-dependent, cellular and humoral mediators/regulators of the human immune system. This interdisciplinary effort exploits engineering analysis methods utilized to investigate anomalous physical system behaviors to explore immune system behaviors. Cancer Immune Control Dynamics (CICD), a systems analysis approach, attempts to identify differences between systemic immune homeostasis of 27 healthy volunteers versus 14 patients with metastatic malignant melanoma based on daily serial measurements of conventional peripheral blood biomarkers (15 cell subsets, 35 cytokines). The modeling strategy applies engineering control theory to analyze an individual's immune system based on the biomarkers' dynamic non-linear oscillatory behaviors. The reverse engineering analysis uses a Singular Value Decomposition (SVD) algorithm to solve the inverse problem and identify a solution profile of the active biomarker relationships. Herein, 28,605 biologically possible biomarker interactions are modeled by a set of matrix equations creating a system interaction model. CICD quantifies the model with a participant's biomarker data then computationally solves it to measure each relationship's activity allowing a visualization of the individual's current state of immunity. RESULTS: CICD results provide initial evidence that this model-based analysis is consistent with identified roles of biomarkers in systemic immunity of cancer patients versus that of healthy volunteers. The mathematical computations alone identified a plausible network of immune cells, including T cells, natural killer (NK) cells, monocytes, and dendritic cells (DC) with cytokines MCP-1 [CXCL2], IP-10 [CXCL10], and IL-8 that play a role in sustaining the state of immunity in advanced cancer. CONCLUSIONS: With CICD modeling capabilities, the complexity of the immune system is mathematically quantified through thousands of possible interactions between multiple biomarkers. Therefore, the overall state of an individual's immune system regardless of clinical status, is modeled as reflected in their blood samples. It is anticipated that CICD-based capabilities will provide tools to specifically address cancer and treatment modulated (immune checkpoint inhibitors) parameters of human immunity, revealing clinically relevant biological interactions.

It is envisaged that the grid infrastructure will be a large-scale distributed software system that will provide high-end computational and storage capabilities to differentiated users. A number of distributed computing technologies are being applied to grid development work, including CORBA and Jini. In this work, we introduce an A4 (Agile Architecture and Autonomous Agents) methodology, which can be used for resource management for grid computing. An initial system implementation utilises the performance prediction techniques of the PACE toolkit to provide quantitative data regarding the performance of complex applications running on local grid resources. At the meta-level, a hierarchy of identical agents is used to provide an abstraction of the system architecture. Each agent is able to cooperate with other agents to provide service advertisement and discovery to schedule applications that need to utilise grid resources. A performance monitor and advisor (PMA) is in development to optimize the performance of agent behaviours.