Conservative collision prediction and avoidance for stochastic trajectories in continuous time and space

Existing work in multi-agent collision prediction and avoidance typically assumes discrete-time trajectories with Gaussian uncertainty or that are completely deterministic. We propose an approach that allows detection of collisions even between continuous, stochastic trajectories with the only restriction that means and variances can be computed. To this end, we employ probabilistic bounds to derive criterion functions whose negative sign provably is indicative of probable collisions. For criterion functions that are Lipschitz, an algorithm is provided to rapidly find negative values or prove their absence. We propose an iterative policy-search approach that avoids prior discretisations and yields collision-free trajectories with adjustably high certainty. We test our method with both fixed-priority and auction-based protocols for coordinating the iterative planning process. Results are provided in collision-avoidance simulations of feedback controlled plants.Comment: This preprint is an extended version of a conference paper that is to appear in \textit{Proceedings of the 13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2014)

The Trouble with Cases

For several decades now a debate has raged about policy-making by litigation. Spurred by the way in which tobacco, environmental, and other litigation has functioned as an alternative form of regulation, the debate asks whether policy-making or regulation by litigation is more or less socially desirable than more traditional policy-making by ex ante rule-making by legislatures or administrative agencies. In this paper we step into this debate, but not to come down on one side or another, all things considered. Rather, we seek to show that any form of regulation that is dominated by high-salience particular cases is highly likely, to make necessarily general policy on the basis of unwarranted assumptions about the typicality of one or a few high-salience cases or events. Two cornerstone concepts of behavioral decision--the availability heuristic and related problems of representativeness--explain this bias. This problem is virtually inevitable in regulation by litigation, yet it is commonly found as well in ex ante rule-making, because such rule-making increasingly takes place in the wake of, and dominated by, particularly notorious and often unrepresentative outlier events. In weighing the net advantages of regulation by ex ante rule-making against those of regulation by litigation, society must recognize that any regulatory form is less effective insofar as it is unable to transcend the distorting effect of high-salience unrepresentative examples.

Metaheuristic Optimization of Power and Energy Systems: Underlying Principles and Main Issues of the `Rush to Heuristics'

In the power and energy systems area, a progressive increase of literature contributions that contain applications of metaheuristic algorithms is occurring. In many cases, these applications are merely aimed at proposing the testing of an existing metaheuristic algorithm on a specific problem, claiming that the proposed method is better than other methods that are based on weak comparisons. This ‘rush to heuristics’ does not happen in the evolutionary computation domain, where the rules for setting up rigorous comparisons are stricter but are typical of the domains of application of the metaheuristics. This paper considers the applications to power and energy systems and aims at providing a comprehensive view of the main issues that concern the use of metaheuristics for global optimization problems. A set of underlying principles that characterize the metaheuristic algorithms is presented. The customization of metaheuristic algorithms to fit the constraints of specific problems is discussed. Some weaknesses and pitfalls that are found in literature contributions are identified, and specific guidelines are provided regarding how to prepare sound contributions on the application of metaheuristic algorithms to specific problems

Metaheuristic optimization of power and energy systems: underlying principles and main issues of the 'rush to heuristics'

In the power and energy systems area, a progressive increase of literature contributions containing applications of metaheuristic algorithms is occurring. In many cases, these applications are merely aimed at proposing the testing of an existing metaheuristic algorithm on a specific problem, claiming that the proposed method is better than other methods based on weak comparisons. This 'rush to heuristics' does not happen in the evolutionary computation domain, where the rules for setting up rigorous comparisons are stricter, but are typical of the domains of application of the metaheuristics. This paper considers the applications to power and energy systems, and aims at providing a comprehensive view of the main issues concerning the use of metaheuristics for global optimization problems. A set of underlying principles that characterize the metaheuristic algorithms is presented. The customization of metaheuristic algorithms to fit the constraints of specific problems is discussed. Some weaknesses and pitfalls found in literature contributions are identified, and specific guidelines are provided on how to prepare sound contributions on the application of metaheuristic algorithms to specific problems

An improved multi-dimensional CMAC neural network: Receptive field function and placement

The standard CMAC has been shown to have fast learning computation as a result of modular receptive field placement, rectangular receptive field shape and a simple weight adaptation algorithm. The standard CMAC, however, suffers from slow convergence at some critical frequency due to the rectangular receptive field shape. A linearly-tapered field, which requires a uniform placement, was used in this research. The receptive field placement of the standard CMAC becomes less uniform locally for a larger receptive field width. This dissertation suggests a new field placement which is more uniform without extra computation. Results show that the slow convergence at the critical frequency is eliminated, and the interaction of the linearly-tapered field with the new placement achieves more accurate function approximation. A theoretical bound on the receptive field width as a function of the input dimension is proposed if a uniform placement is to be achieved. Also, a procedure for adapting receptive field density to minimize the weight usage for a given approximation accuracy is suggested

Winner Determination in Combinatorial Auctions using Hybrid Ant Colony Optimization and Multi-Neighborhood Local Search

A combinatorial auction is an auction where the bidders have the choice to bid on bundles of items. The WDP in combinatorial auctions is the problem of finding winning bids that maximize the auctioneer’s revenue under the constraint that each item can be allocated to at most one bidder. The WDP is known as an NP-hard problem with practical applications like electronic commerce, production management, games theory, and resources allocation in multi-agent systems. This has motivated the quest for efficient approximate algorithms both in terms of solution quality and computational time. This paper proposes a hybrid Ant Colony Optimization with a novel Multi-Neighborhood Local Search (ACO-MNLS) algorithm for solving Winner Determination Problem (WDP) in combinatorial auctions. Our proposed MNLS algorithm uses the fact that using various neighborhoods in local search can generate different local optima for WDP and that the global optima of WDP is a local optima for a given its neighborhood. Therefore, proposed MNLS algorithm simultaneously explores a set of three different neighborhoods to get different local optima and to escape from local optima. The comparisons between ACO-MNLS, Genetic Algorithm (GA), Memetic Algorithm (MA), Stochastic Local Search (SLS), and Tabu Search (TS) on various benchmark problems confirm the efficiency of ACO-MNLS in the terms of solution quality and computational time