Predictive Collision Management for Time and Risk Dependent Path Planning

Autonomous agents such as self-driving cars or parcel robots need to recognize and avoid possible collisions with obstacles in order to move successfully in their environment. Humans, however, have learned to predict movements intuitively and to avoid obstacles in a forward-looking way. The task of collision avoidance can be divided into a global and a local level. Regarding the global level, we propose an approach called "Predictive Collision Management Path Planning" (PCMP). At the local level, solutions for collision avoidance are used that prevent an inevitable collision. Therefore, the aim of PCMP is to avoid unnecessary local collision scenarios using predictive collision management. PCMP is a graph-based algorithm with a focus on the time dimension consisting of three parts: (1) movement prediction, (2) integration of movement prediction into a time-dependent graph, and (3) time and risk-dependent path planning. The algorithm combines the search for a shortest path with the question: is the detour worth avoiding a possible collision scenario? We evaluate the evasion behavior in different simulation scenarios and the results show that a risk-sensitive agent can avoid 47.3% of the collision scenarios while making a detour of 1.3%. A risk-averse agent avoids up to 97.3% of the collision scenarios with a detour of 39.1%. Thus, an agent's evasive behavior can be controlled actively and risk-dependent using PCMP.Comment: Extended version of the SIGSPATIAL '20 pape

Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization

Multi-objective reinforcement learning (MORL) algorithms tackle sequential decision problems where agents may have different preferences over (possibly conflicting) reward functions. Such algorithms often learn a set of policies (each optimized for a particular agent preference) that can later be used to solve problems with novel preferences. We introduce a novel algorithm that uses Generalized Policy Improvement (GPI) to define principled, formally-derived prioritization schemes that improve sample-efficient learning. They implement active-learning strategies by which the agent can (i) identify the most promising preferences/objectives to train on at each moment, to more rapidly solve a given MORL problem; and (ii) identify which previous experiences are most relevant when learning a policy for a particular agent preference, via a novel Dyna-style MORL method. We prove our algorithm is guaranteed to always converge to an optimal solution in a finite number of steps, or an $\epsilon$-optimal solution (for a bounded $\epsilon$) if the agent is limited and can only identify possibly sub-optimal policies. We also prove that our method monotonically improves the quality of its partial solutions while learning. Finally, we introduce a bound that characterizes the maximum utility loss (with respect to the optimal solution) incurred by the partial solutions computed by our method throughout learning. We empirically show that our method outperforms state-of-the-art MORL algorithms in challenging multi-objective tasks, both with discrete and continuous state and action spaces.Comment: Accepted to AAMAS 202

Resource management in QoS-aware wireless cellular networks

2011 Summer.Includes bibliographical references.Emerging broadband wireless networks that support high speed packet data with heterogeneous quality of service (QoS) requirements demand more flexible and efficient use of the scarce spectral resource. Opportunistic scheduling exploits the time-varying, location-dependent channel conditions to achieve multiuser diversity. In this work, we study two types of resource allocation problems in QoS-aware wireless cellular networks. First, we develop a rigorous framework to study opportunistic scheduling in multiuser OFDM systems. We derive optimal opportunistic scheduling policies under three common QoS/fairness constraints for multiuser OFDM systems--temporal fairness, utilitarian fairness, and minimum-performance guarantees. To implement these optimal policies efficiently, we provide a modified Hungarian algorithm and a simple suboptimal algorithm. We then propose a generalized opportunistic scheduling framework that incorporates multiple mixed QoS/fairness constraints, including providing both lower and upper bound constraints. Next, taking input queues and channel memory into consideration, we reformulate the transmission scheduling problem as a new class of Markov decision processes (MDPs) with fairness constraints. We investigate the throughput maximization and the delay minimization problems in this context. We study two categories of fairness constraints, namely temporal fairness and utilitarian fairness. We consider two criteria: infinite horizon expected total discounted reward and expected average reward. We derive and prove explicit dynamic programming equations for the above constrained MDPs, and characterize optimal scheduling policies based on those equations. An attractive feature of our proposed schemes is that they can easily be extended to fit different objective functions and other fairness measures. Although we only focus on uplink scheduling, the scheme is equally applicable to the downlink case. Furthermore, we develop an efficient approximation method--temporal fair rollout--to reduce the computational cost

High Studies Deanery Faculty of Commerce Business Administration Department

This study aimed to show the influence of information overload on the decision making process, using the descriptive analytical method to study the influence of information overload represented through three dimensions (email overload, information characteristics and information & communication technology). The study was applied on INGOs that work in Gaza Strip. The questionnaire was used as a data collection tool. The study population was (106) who are all managers (Top and Middle levels Management) in INGOs. A census has been used for the target population. Because of the policy of some organizations regarding confidentiality and privacy of work environment, only (86) questionnaires were collected and all were retrieved and analyzed. Results of the analysis of the questionnaire data demonstrate significant relationship of information overload with the managers ‘decision making process

PLANNING UNDER UNCERTAINTIES FOR AUTONOMOUS DRIVING ON URBAN ROAD

Ph.DDOCTOR OF PHILOSOPH

An Agent-Based Variogram Modeller: Investigating Intelligent, Distributed-Component Geographical Information Systems

Geo-Information Science (GIScience) is the field of study that addresses substantive questions concerning the handling, analysis and visualisation of spatial data. Geo- Information Systems (GIS), including software, data acquisition and organisational arrangements, are the key technologies underpinning GIScience. A GIS is normally tailored to the service it is supposed to perform. However, there is often the need to do a function that might not be supported by the GIS tool being used. The normal solution in these circumstances is to go out and look for another tool that can do the service, and often an expert to use that tool. This is expensive, time consuming and certainly stressful to the geographical data analyses. On the other hand, GIS is often used in conjunction with other technologies to form a geocomputational environment. One of the complex tools in geocomputation is geostatistics. One of its functions is to provide the means to determine the extent of spatial dependencies within geographical data and processes. Spatial datasets are often large and complex. Currently Agent system are being integrated into GIS to offer flexibility and allow better data analysis. The theis will look into the current application of Agents in within the GIS community, determine if they are used to representing data, process or act a service. The thesis looks into proving the applicability of an agent-oriented paradigm as a service based GIS, having the possibility of providing greater interoperability and reducing resource requirements (human and tools). In particular, analysis was undertaken to determine the need to introduce enhanced features to agents, in order to maximise their effectiveness in GIS. This was achieved by addressing the software agent complexity in design and implementation for the GIS environment and by suggesting possible solutions to encountered problems. The software agent characteristics and features (which include the dynamic binding of plans to software agents in order to tackle the levels of complexity and range of contexts) were examined, as well as discussing current GIScience and the applications of agent technology to GIS, agents as entities, objects and processes. These concepts and their functionalities to GIS are then analysed and discussed. The extent of agent functionality, analysis of the gaps and the use these technologies to express a distributed service providing an agent-based GIS framework is then presented. Thus, a general agent-based framework for GIS and a novel agent-based architecture for a specific part of GIS, the variogram, to examine the applicability of the agent- oriented paradigm to GIS, was devised. An examination of the current mechanisms for constructing variograms, underlying processes and functions was undertaken, then these processes were embedded into a novel agent architecture for GIS. Once the successful software agent implementation had been achieved, the corresponding tool was tested and validated - internally for code errors and externally to determine its functional requirements and whether it enhances the GIS process of dealing with data. Thereafter, its compared with other known service based GIS agents and its advantages and disadvantages analysed

Hierarchical Collective Agent Network (HCAN) for efficient 3 fusion and management of multiple networked sensors

Agent-based software systems and applications are constructed by integrating diverse sets of components that are intelligent, heterogeneous, distributed, and concurrent. This paper describes a multi-agent system to assure the operation efficiency and reliability in data fusion and management of a set of networked distributive sensors (NDS). We discuss the general concept and architecture of a Hierarchical Collective Agent Network (HCAN) and its functional components for learning and adaptive control of the NDS. Sophistication of a HCAN control environment and an anatomy of the agent modules for enabling intelligent data fusion and management are presented. An exemplar HCAN is configured to support dynamic data fusion and automated sensor management in a simulated distributive and collaborative military sensor network for Global Missile Defense (GMD) application

Adaptive sampling of transient environmental phenomena with autonomous mobile platforms

Submitted in partial fulfillment of the requirements for the degree of Master of Science in Aeronautics and Astronautics at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 2019.In the environmental and earth sciences, hypotheses about transient phenomena have been universally investigated by collecting physical sample materials and performing ex situ analysis. Although the gold standard, logistical challenges limit the overall efficacy: the number of samples are limited to what can be stored and transported, human experts must be able to safely access or directly observe the target site, and time in the field and subsequently the laboratory, increases overall campaign expense. As a result, the temporal detail and spatial diversity in the samples may fail to capture insightful structure of the phenomenon of interest. The development of in situ instrumentation allows for near real-time analysis of physical phenomenon through observational strategies (e.g., optical), and in combination with unmanned mobile platforms, has considerably impacted field operations in the sciences. In practice, mobile platforms are either remotely operated or perform guided, supervised autonomous missions specified as navigation between humanselected waypoints. Missions like these are useful for gaining insight about a particular target site, but can be sample-sparse in scientifically valuable regions, particularly in complex or transient distributions. A skilled human expert and pilot can dynamically adjust mission trajectories based on sensor information. Encoding their insight onto a vehicle to enable adaptive sampling behaviors can broadly increase the utility of mobile platforms in the sciences. This thesis presents three field campaigns conducted with a human-piloted marine surface vehicle, the ChemYak, to study the greenhouse gases methane (CH4) and carbon dioxide (CO2) in estuaries, rivers, and the open ocean. These studies illustrate the utility of mobile surface platforms for environmental research, and highlight key challenges of studying transient phenomenon. This thesis then formalizes the maximum seek-and-sample (MSS) adaptive sampling problem, which requires a mobile vehicle to efficiently find and densely sample from the most scientifically valuable region in an a priori unknown, dynamic environment. The PLUMES algorithm — Plume Localization under Uncertainty using Maximum-ValuE information and Search—is subsequently presented, which addresses the MSS problem and overcomes key technical challenges with planning in natural environments. Theoretical performance guarantees are derived for PLUMES, and empirical performance is demonstrated against canonical uniform search and state-of-the-art baselines in simulation and field trials. Ultimately, this thesis examines the challenges of autonomous informative sampling in the environmental and earth sciences. In order to create useful systems that perform diverse scientific objectives in natural environments, approaches from robotics planning, field design, Bayesian optimization, machine learning, and the sciences must be drawn together. PLUMES captures the breadth and depth required to solve a specific objective within adaptive sampling, and this work as a whole highlights the potential for mobile technologies to perform intelligent autonomous science in the future