Successive elimination of candidates is often a route to making manipulation intractable to compute. We prove that eliminating candidates does not necessarily increase the computational complexity of manipulation. However, for many voting rules used in practice, the computational complexity increases. For example, it is already known that it is NP-hard to compute how a single voter can manipulate the result of single transferable voting (the elimination version of plurality voting). We show here that it is NP-hard to compute how a single voter can manipulate the result of the elimination version of veto voting, of the closely related Coombs’ rule, and of the elimination versions of a general class of scoring rules.

Randomized first-mover strategies of Stackelberg games are used in several deployed applications to allocate limited resources for the protection of critical infrastructure. Stackelberg games model the fact that a strategic attacker can surveil and exploit the defender's strategy, and randomization guards against the worst effects by making the defender less predictable. In accordance with the standard game-theoretic model of Stackelberg games, past work has typically assumed that the attacker has perfect knowledge of the defender's randomized strategy and will react correspondingly. In light of the fact that surveillance is costly, risky, and delays an attack, this assumption is clearly simplistic: attackers will usually act on partial knowledge of the defender's strategies. The attacker's imperfect estimate could present opportunities and possibly also threats to a strategic defender. In this paper, we therefore begin a systematic study of security games with limited surveillance. We propose a natural model wherein an attacker forms or updates a belief based on observed actions, and chooses an optimal response. We investigate the model both theoretically and experimentally. In particular, we give mathematical programs to compute optimal attacker and defender strategies for a fixed observation duration, and show how to use them to estimate the attacker's observation durations. Our experimental results show that the defender can achieve significant improvement in expected utility by taking the attacker's limited surveillance into account, validating the motivation of our work.

We consider the problem of designing automated strategies for interactions with human subjects, where the humans must be rewarded for performing certain tasks of interest. We focus on settings where there is a single task that must be performed many times by different humans (e.g. answering a questionnaire), and the humans require a fee for performing the task. In such settings, our objective is to minimize the average cost for effectuating the completion of the task. We present two automated strategies for designing efficient agents for the problem, based on two different models of human behavior. The first, the Reservation Price Based Agent (RPBA), is based on the concept of a reservation price, and the second, the No Bargaining Agent (NBA), uses principles from behavioral science. The performance of the agents has been tested in extensive experiments with real human subjects, where NBA outperforms both RPBA and strategies developed by human experts.

In aviation industry, free flight is a new concept which implies considering more freedom in the selection and modification of flight paths during flight time. The free flight concept allows pilots choose their own flight paths more efficient, and also plan for their flight with high performance. Although free flight has many advantages such as minimum delays and the reduction of the workload of the air traffic control centers, this concept causes many problems which one of the most important of them are conflicts between different aircrafts. Thus, Conflict Detection and Resolution (CD&R) is a major challenge in air traffic management. In this paper, we presented a model for CD&R between aircrafts in air traffic management using Graph Coloring Problem (GCP) method. In fact, we mapped the congestion area to a corresponding graph, and then addressed to find a reliable and optimal coloring for this graph using one of the new evolutionary algorithms known as Imperialist Competitive Algorithm (ICA) to solve the conflicts. Using ICA for solving GCP is a new method.

For the problem of moving a set of agents on a connected graphto agent-specific goal locations, free of collisions, we propose a multiflow based integer linear programming (ILP) model that finds a time optimal solution. The resulting algorithm from our ILP model is complete and guarantees to yield true optimal solutions. Focusing on the time optimal formulation, we evaluate its performance, both as a stand alone algorithm and as a generic heuristic for quickly solving large problem instances. The computational results confirm the effectiveness of our method.

Problems that require multiple agents to follow non-interfering paths from their current states to their respective goal states are called multi-agent pathfinding problems (MAPFs). In previous work, we presented Independence Detection (ID), an algorithm for breaking a large MAPF problem into smaller problems that can be solved independently. Independence Detection is complete and can be used in combination with both optimal and approximation algorithms. This paper serves as an introduction to Independence Detection and aims to clarify its details.

Collision avoidance and multi-agent navigation is an important component of modern video games. Recent developments in commodity hardware, in particular the utilization of multi-core and many-core architectures in personal computers and consoles are allowing large numbers of virtual agents to be incorporated into game levels in increasing numbers. We present the hybrid reciprocal velocity obstacle and optimal reciprocal collision avoidance methods for reciprocal collision avoidance and navigation in video games and described their implementations in C++ as HRVO Library and RVO2 Library. The libraries can efficiently simulate groups of twenty-five to one thousand virtual agents in dense conditions and around moving and static obstacles.

In the multi agent path finding problem (MAPF) paths shouldbe found for several agents, each with a different start andgoal position such that agents do not collide. Previous optimalsolvers applied global A*-based searches. We presenta new search algorithm called Conflict Based Search (CBS).CBS is a two-level algorithm. At the high level, a search isperformed on a tree based on conflicts between agents. At thelow level, a search is performed only for a single agent at atime. In many cases this reformulation enables CBS to examinefewer states than A* while still maintaining optimality.We analyze CBS and show its benefits and drawbacks. Experimentalresults on various problems shows a speedup ofup to a full order of magnitude over previous approaches.

Motivated by efficient algorithms for solving combina- torial and discrete instances of the multi-agent pathfinding problem, this report investigates ways to utilize such solutions to solve similar problems in the continuous domain. While a simple discretization of the space which allows the direct application of combinatorial algorithms seems like a straightforward solution, there are additional constraints that such a discretization needs to satisfy in order to be able to provide some form of completeness guarantees in general configuration spaces. This report reviews ideas on how to utilize combinatorial algorithms to solve continuous multi-agent pathfinding problems. It aims to collect feedback from the community regarding the importance and the complexity of this challenge, as well as the appropriateness of the solutions considered here.

A* is the algorithm of finding the shortest path between two nodes in a graph. When the searching problem is constituted of a set of linked graphs, A* searches solution like if it is face of one graph formed by linked graphs. While researchers have developed solutions to reduce the execution time of A* in multiple cases by multiples techniques, we develop a new algorithm: DEC-A* which is a decentralized version of A* composing a solution through a collection of graph. A* uses a distance-plus-cost heuristic function to determine the order in which the search visits nodes in the tree. Our algorithm DEC-A* extends the evaluation of the distance-plus-cost heuristic to be the sum of two functions : local distance, which evaluates the cost to reach the nearest neighbor node s to the goal, and global distance which evaluates the cost from s to the goal through other graphs. DEC-A* reduces the time of finding the shortest path and reduces the complexity, while ensuring the privacy of graphs.