Physical search problems with probabilistic knowledge

2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 2015

This paper presents an experimental study on collaborative search by distributed autonomous agents. We consider a problem such that multiple agents search for target objects in a field, and communicate with each other to exchange information of objects. Agents have six different strategies: cooperative, skeptical, free rider, liar, skeptical liar and solitary. These strategies characterize behaviors of agents for transmitting/receiving information in different ways. We observe the effect of self-interested agents who act non-cooperatively or even act deceptively to increase their own profits. We identify situations in which self-interested agents have advantages and investigate conditions which are effective to suppress the development of self-interested agents.

2010

To run a company, employers need to hire people to realize a product, a sport coach can motivate some players intend to win a match, a designer have to force synchronization of some parallel processes to avoid deadlocking. In these examples, a device external to the global system tries to force the behavior of some agents, aiming at achieving a global objective. For realism purposes, we assume that forming a coalition has a cost that should be minimal. This cost models the fact that individuals of the coalition may be deviated from their original objective. Game theory is a vast field that addresses this kind of problem. In this work we focus on turn-based games, a simple and broadly explored branch of game theory. Turn-based games offer well-known tools, among which the most famous minmax algorithm where a constrained optimization problem is addressed [19]. The problem we aim at solving is to find a minimal cost coalition which enforces some position in a given set (the goal) to be...

2014

In traditional algorithm design, no incentives come into play: the input is given, and your algorithm must produce a correct output. How much harder is it to solve the same problem when the input is not given directly, but instead reported by strategic agents with interests of their own? The unique challenge stems from the fact that the agents may choose to lie about the input in order to manipulate the behavior of the algorithm for their own interests, and tools from Game Theory are therefore required in order to predict how these agents will behave. We develop a new algorithmic framework with which to study such problems. Specifically, we provide a computationally efficient black-box reduction from solving any optimization problem on "strategic input," often called algorithmic mechanism design to solving a perturbed version of that same optimization problem when the input is directly given, traditionally called algorithm design. We further demonstrate the power of our framework by making significant progress on several long-standing open problems. First, we extend Myerson's celebrated characterization of single item auctions [Mye81] to multiple items, providing also a computationally efficient implementation of optimal auctions. Next, we design a computationally efficient 2-approximate mechanism for job scheduling on unrelated machines, the original problem studied in Nisan and Ronen's paper introducing the field of Algorithmic Mechanism Design [NR99]. This matches the guarantee of the best known computationally efficient algorithm when the input is directly given. Finally, we provide the first hardness of approximation result for optimal mechanism design.

Communications of the ACM, 2010

The widespread adoption of the Internet and the emergence of the Web changed society's relationship with computers. The primary role of a computer evolved from a stand-alone, well-understood machine for executing software to a conduit for global communication, content-dissemination, and commerce. The algorithms and complexity theory community has responded to these changes by formulating novel problems, goals, and design and analysis techniques relevant for modern applications. Game theory, which has studied deeply the interaction between competing or cooperating individuals, plays a central role in these new developments. Research on the interface of theoretical computer science and game theory, an area now known as algorithmic game theory (AGT), has exploded phenomenally over the past ten years.

arXiv (Cornell University), 2022

Many scenarios where agents with restrictions compete for resources can be cast as maximum matching problems on bipartite graphs. Our focus is on resource allocation problems where agents may have restrictions that make them incompatible with some resources. We assume that a Principal chooses a maximum matching randomly so that each agent is matched to a resource with some probability. Agents would like to improve their chances of being matched by modifying their restrictions within certain limits. The Principal's goal is to advise an unsatisfied agent to relax its restrictions so that the total cost of relaxation is within a budget (chosen by the agent) and the increase in the probability of being assigned a resource is maximized. We establish hardness results for some variants of this budget-constrained maximization problem and present algorithmic results for other variants. We experimentally evaluate our methods on synthetic datasets as well as on two novel real-world datasets: a vacation activities dataset and a classrooms dataset.

Computer Networks, 2008

Optical networks Add-drop multiplexer (ADM) Nash equilibria Price of anarchy Price of collusion a b s t r a c t

Proceedings of the fourth international conference on Autonomous agents - AGENTS '00, 2000

This paper considers resource allocation in a network with mobile agents competing for computational priority. We formulate this problem as a multi-agent game with the players being agents purchasing service from a common server. We show that there exists a computable Nash equilibrium when agents have perfect information into the future. From our game, we build a market-based CPU allocation policy and a strategy with which an agent may plan its expenditures for a multi-hop itinerary. We simulate a network of hosts and agents using our strategy to show that our resourceallocation mechanism effectively prioritizes agents according to their endowments and that our planning algorithm handles network delay gracefully.

Proceedings of the 2017 ACM Conference on Economics and Computation, 2017

An ever-important issue is protecting infrastructure and other valuable targets from a range of threats from vandalism to theft to piracy to terrorism. The "defender" can rarely afford the needed resources for a 100% protection. Thus, the key question is, how to provide the best protection using the limited available resources. We study a practically important class of security games that is played out in space and time, with targets and "patrols" moving on a real line. A central open question here is whether the Nash equilibrium (i.e., the minimax strategy of the defender) can be computed in polynomial time. We resolve this question in the affirmative. Our algorithm runs in time polynomial in the input size, and only polylogarithmic in the number of possible patrol locations (). Further, we provide a continuous extension in which patrol locations can take arbitrary real values. Prior work obtained polynomial-time algorithms only under a substantial assumption, e.g., a constant number of rounds. Further, all these algorithms have running times polynomial in , which can be very large.

Proceedings of the 2012 ACM symposium on Principles of distributed computing - PODC '12, 2012

We generalize the classical cow-path problem [7, 14, 38, 39] into a question that is relevant for collective foraging in animal groups. Specifically, we consider a setting in which k identical (probabilistic) agents, initially placed at some central location, collectively search for a treasure in the two-dimensional plane. The treasure is placed at a target location by an adversary and the goal is to find it as fast as possible as a function of both k and D, where D is the distance between the central location and the target. This is biologically motivated by cooperative, central place foraging such as performed by ants around their nest. In this type of search there is a strong preference to locate nearby food sources before those that are further away. Our focus is on trying to find what can be achieved if communication is limited or altogether absent. Indeed, to avoid overlaps agents must be highly dispersed making communication difficult. Furthermore, if agents do not commence the search in synchrony then even initial communication is problematic. This holds, in particular, with respect to the question of whether the agents can communicate and conclude their total number, k. It turns out that the knowledge of k by the individual agents is crucial for performance. Indeed, it is a straightforward observation that the time required for finding the treasure is Ω(D + D 2 /k), and we show in this paper that this bound can be matched if the agents have knowledge of k up to some constant approximation. We present an almost tight bound for the competitive penalty that must be paid, in the running time, if agents have no information about k. Specifically, on the negative side, we show that in such a case, there is no algorithm whose competitiveness is O(log k). On the other hand, we show that for every constant ǫ > 0, there exists a rather simple uniform search algorithm which is O(log 1+ǫ k)-competitive. In addition, we give a lower bound for the setting in which agents are given some estimation of k. As a special case, this lower bound implies that for any constant ǫ > 0, if each agent is given a (one-sided) k ǫ-approximation to k, then the competitiveness is Ω(log k). Informally, our results imply that the agents can potentially perform well without any knowledge of their total number k, however, to further improve, they must be given a relatively good approximation of k. Finally, we propose a uniform algorithm that is both efficient and extremely simple suggesting its relevance for actual biological scenarios.

ArXiv, 2021

Consider a two-person zero-sum search game between a hider and a searcher. The hider hides among n discrete locations, and the searcher successively visits individual locations until finding the hider. Known to both players, a search at location i takes ti time units and detects the hider—if hidden there—independently with probability qi, for i = 1, . . . , n. The hider aims to maximize the expected time until detection, while the searcher aims to minimize it. We prove the existence of an optimal strategy for each player. In particular, the hider’s optimal mixed strategy hides in each location with a nonzero probability, and the searcher’s optimal mixed strategy can be constructed with up to n simple search sequences. We develop an algorithm to compute an optimal strategy for each player, and compare the optimal hiding strategy with the simple hiding strategy which gives the searcher no location preference at the outset.