Emergent Behavior Development and Control in Multi-Agent Systems

Emergence in natural systems is the development of complex behaviors that result from the aggregation of simple agent-to-agent and agent-to-environment interactions. Emergence research intersects with many disciplines such as physics, biology, and ecology and provides a theoretical framework for investigating how order appears to spontaneously arise in complex adaptive systems. In biological systems, emergent behaviors allow simple agents to collectively accomplish multiple tasks in highly dynamic environments; ensuring system survival. These systems all display similar properties: self-organized hierarchies, robustness, adaptability, and decentralized task execution. However, current algorithmic approaches merely present theoretical models without showing how these models actually create hierarchical, emergent systems. To fill this research gap, this dissertation presents an algorithm based on entropy and speciation - defined as morphological or physiological differences in a population - that results in hierarchical emergent phenomena in multi-agent systems. Results show that speciation creates system hierarchies composed of goal-aligned entities, i.e. niches. As niche actions aggregate into more complex behaviors, more levels emerge within the system hierarchy, eventually resulting in a system that can meet multiple tasks and is robust to environmental changes. Speciation provides a powerful tool for creating goal-aligned, decentralized systems that are inherently robust and adaptable, meeting the scalability demands of current, multi-agent system design. Results in base defense, k-n assignment, division of labor and resource competition experiments, show that speciated populations create hierarchical self-organized systems, meet multiple tasks and are more robust to environmental change than non-speciated populations

Pursuer Assignment and Control Strategies in Multi-Agent Pursuit-Evasion Under Uncertainties.

We consider a pursuit-evasion problem with a heterogeneous team of multiple pursuers and multiple evaders. Although both the pursuers and the evaders are aware of each others' control and assignment strategies, they do not have exact information about the other type of agents' location or action. Using only noisy on-board sensors the pursuers (or evaders) make probabilistic estimation of positions of the evaders (or pursuers). Each type of agent use Markov localization to update the probability distribution of the other type. A search-based control strategy is developed for the pursuers that intrinsically takes the probability distribution of the evaders into account. Pursuers are assigned using an assignment algorithm that takes redundancy (i.e., an excess in the number of pursuers than the number of evaders) into account, such that the total or maximum estimated time to capture the evaders is minimized. In this respect we assume the pursuers to have clear advantage over the evaders. However, the objective of this work is to use assignment strategies that minimize the capture time. This assignment strategy is based on a modified Hungarian algorithm as well as a novel algorithm for determining assignment of redundant pursuers. The evaders, in order to effectively avoid the pursuers, predict the assignment based on their probabilistic knowledge of the pursuers and use a control strategy to actively move away from those pursues. Our experimental evaluation shows that the redundant assignment algorithm performs better than an alternative nearest-neighbor based assignment algorithm

Sensor-Based Topological Coverage And Mapping Algorithms For Resource-Constrained Robot Swarms

Coverage is widely known in the field of sensor networks as the task of deploying sensors to completely cover an environment with the union of the sensor footprints. Related to coverage is the task of exploration that includes guiding mobile robots, equipped with sensors, to map an unknown environment (mapping) or clear a known environment (searching and pursuit- evasion problem) with their sensors. This is an essential task for robot swarms in many robotic applications including environmental monitoring, sensor deployment, mine clearing, search-and-rescue, and intrusion detection. Utilizing a large team of robots not only improves the completion time of such tasks, but also improve the scalability of the applications while increasing the robustness to systems’ failure. Despite extensive research on coverage, mapping, and exploration problems, many challenges remain to be solved, especially in swarms where robots have limited computational and sensing capabilities. The majority of approaches used to solve the coverage problem rely on metric information, such as the pose of the robots and the position of obstacles. These geometric approaches are not suitable for large scale swarms due to high computational complexity and sensitivity to noise. This dissertation focuses on algorithms that, using tools from algebraic topology and bearing-based control, solve the coverage related problem with a swarm of resource-constrained robots. First, this dissertation presents an algorithm for deploying mobile robots to attain a hole-less sensor coverage of an unknown environment, where each robot is only capable of measuring the bearing angles to the other robots within its sensing region and the obstacles that it touches. Next, using the same sensing model, a topological map of an environment can be obtained using graph-based search techniques even when there is an insufficient number of robots to attain full coverage of the environment. We then introduce the landmark complex representation and present an exploration algorithm that not only is complete when the landmarks are sufficiently dense but also scales well with any swarm size. Finally, we derive a multi-pursuers and multi-evaders planning algorithm, which detects all possible evaders and clears complex environments

Annual Report 2001-2002

The Institute for Economic Analysis (IAE) is a center of the Spanish Council for Scientific Research (CSIC). The Institute was created in 1985 and is dedicated to the pursuit of academic excellence in Economics. This document presents the Institute’s biennial report for the years 2001 and 2002. The Institute’s activities cover both theoretical and empirical research in various areas, including industrial organization, finance, regional economics, political economics, macroeconomics and growth, public economics, game theory, and experimental economics. The numerous studies undertaken by the Institute’s staff have translated into a large number of publications in international scientific journals and books.N

Probabilistic Graph-Clear

Abstract — This paper introduces a probabilistic model for multirobot surveillance applications with limited range and possibly faulty sensors. Sensors are described with a footprint and a false negative probability, i.e. the probability of failing to report a target within their sensing range. The model implements a probabilistic extension to our formerly developed deterministic approach for modeling surveillance tasks in large environments with large robot teams known as Graph-Clear. This extension leads to a new algorithm that allows to answer new design and performance questions, namely 1) how many robots are needed to obtain a certain confidence that the environment is free from intruders, and 2) given a certain number of robots, how should they coordinate their actions to minimize their failure rate. I

Fiscal Decentralization and Peasants' Financial Burden in China

This paper sheds light on the heavy financial burden on peasants in China's fiscal decentralization system. Using a political economy framework, this paper explores the tax-farming nature of China's fiscally decentralized system and examines why the system incurs a particularly heavy financial burden on peasants. Specifically, it points out that a political hierarchy financed by a tax-farming system in China, fails to contain the exploitative behavior of local officials, which results in the expenditure devolution and revenue centralization within the hierarchy. Ultimately, peasants bear the brunt of the tax burden. As the financial pressure of excessive levies and fees reaches a perilous point, peasants are resorting to violent protests. Unless a fiscally decentralized system with horizontal accountability mechanisms evolves, the country's ability to sustain a centralized polity may become increasingly undermined. A case study of township finance is used to exemplify the exploitative nature of China's fiscal decentralization system.Fiscal Decentralization, Corruption, Financial Burden, China

Air Force Institute of Technology Research Report 2018

This Research Report presents the FY18 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs). Interested individuals may discuss ideas for new research collaborations, potential CRADAs, or research proposals with individual faculty using the contact information in this document

Exploiting opportunities at all cost? Entrepreneurial intent and externalities

Do potential entrepreneurs exploit welfare-destroying opportunities as much as they exploit welfare-enhancing opportunities as it is assumed in several normative models? Do we need to prevent potential entrepreneurs from being destructive or are there intrinsic limits to harm others? We experimentally investigate how people with different entrepreneurial intent exploit risky investment opportunities that are associated with negative and positive externalities. We find that participants who consider entrepreneurship as a future occupation invest significantly less than others in destructive opportunities. Nevertheless, our results support prior evidence that the entrepreneurially talented invest more in destructive opportunities. The latter effect seems to be entrepreneurship-specific, because the investment behavior of the generally more talented does not differ from that of other participants. Taken together, our results suggest that people who are willing to exploit destructive opportunities do not only do this in private ventures, but also - and maybe even more so - in wage employment.Social Psychology, Entrepreneurship, Externalities, Laboratory, Individual Behavior

The Trail, 1958-12-16

https://soundideas.pugetsound.edu/thetrail_all/1786/thumbnail.jp