65,854 research outputs found
An Algorithmic Framework for Strategic Fair Division
We study the paradigmatic fair division problem of allocating a divisible
good among agents with heterogeneous preferences, commonly known as cake
cutting. Classical cake cutting protocols are susceptible to manipulation. Do
their strategic outcomes still guarantee fairness?
To address this question we adopt a novel algorithmic approach, by designing
a concrete computational framework for fair division---the class of Generalized
Cut and Choose (GCC) protocols}---and reasoning about the game-theoretic
properties of algorithms that operate in this model. The class of GCC protocols
includes the most important discrete cake cutting protocols, and turns out to
be compatible with the study of fair division among strategic agents. In
particular, GCC protocols are guaranteed to have approximate subgame perfect
Nash equilibria, or even exact equilibria if the protocol's tie-breaking rule
is flexible. We further observe that the (approximate) equilibria of
proportional GCC protocols---which guarantee each of the agents a
-fraction of the cake---must be (approximately) proportional. Finally, we
design a protocol in this framework with the property that its Nash equilibrium
allocations coincide with the set of (contiguous) envy-free allocations
Investigating strategies for cooperative planning of independent agents through prototype evaluation
This paper discusses the application of the prototyping approach to investigating
the requirements on strategies for cooperative planning and con
ict resolution of
independent agents by means of an example application: the strategic game \Scotland
Yard". The strategies for coordinating the agents, which are parallel algorithms,
are developed with a prototyping approach using ProSet-Linda. ProSet-Linda is
designed for prototyping parallel algorithms.
We concentrate on the techniques employed to elicit the requirements on the algorithms for agent interaction. The example application serves to illustrate the prototyping approach to requirements elicitation by means of a non-trivial instance for
investigating algorithms for cooperative planning and con
ict resolution
Open-ended Learning in Symmetric Zero-sum Games
Zero-sum games such as chess and poker are, abstractly, functions that
evaluate pairs of agents, for example labeling them `winner' and `loser'. If
the game is approximately transitive, then self-play generates sequences of
agents of increasing strength. However, nontransitive games, such as
rock-paper-scissors, can exhibit strategic cycles, and there is no longer a
clear objective -- we want agents to increase in strength, but against whom is
unclear. In this paper, we introduce a geometric framework for formulating
agent objectives in zero-sum games, in order to construct adaptive sequences of
objectives that yield open-ended learning. The framework allows us to reason
about population performance in nontransitive games, and enables the
development of a new algorithm (rectified Nash response, PSRO_rN) that uses
game-theoretic niching to construct diverse populations of effective agents,
producing a stronger set of agents than existing algorithms. We apply PSRO_rN
to two highly nontransitive resource allocation games and find that PSRO_rN
consistently outperforms the existing alternatives.Comment: ICML 2019, final versio
Mechanism design for decentralized online machine scheduling
Traditional optimization models assume a central decision maker who optimizes a global system performance measure. However, problem data is often distributed among several agents, and agents take autonomous decisions. This gives incentives for strategic behavior of agents, possibly leading to sub-optimal system performance. Furthermore, in dynamic environments, machines are locally dispersed and administratively independent. Examples are found both in business and engineering applications. We investigate such issues for a parallel machine scheduling model where jobs arrive online over time. Instead of centrally assigning jobs to machines, each machine implements a local sequencing rule and jobs decide for machines themselves. In this context, we introduce the concept of a myopic best response equilibrium, a concept weaker than the classical dominant strategy equilibrium, but appropriate for online problems. Our main result is a polynomial time, online mechanism that |assuming rational behavior of jobs| results in an equilibrium schedule that is 3.281-competitive with respect to the maximal social welfare. This is only lightly worse than state-of-the-art algorithms with central coordination
Using Multi-Agent Reinforcement Learning in Auction Simulations
Game theory has been developed by scientists as a theory of strategic
interaction among players who are supposed to be perfectly rational. These
strategic interactions might have been presented in an auction, a business
negotiation, a chess game, or even in a political conflict aroused between
different agents. In this study, the strategic (rational) agents created by
reinforcement learning algorithms are supposed to be bidder agents in various
types of auction mechanisms such as British Auction, Sealed Bid Auction, and
Vickrey Auction designs. Next, the equilibrium points determined by the agents
are compared with the outcomes of the Nash equilibrium points for these
environments. The bidding strategy of the agents is analyzed in terms of
individual rationality, truthfulness (strategy-proof), and computational
efficiency. The results show that using a multi-agent reinforcement learning
strategy improves the outcomes of the auction simulations
A Strategic Routing Framework and Algorithms for Computing Alternative Paths
Traditional navigation services find the fastest route for a single driver. Though always using the fastest route seems desirable for every individual, selfish behavior can have undesirable effects such as higher energy consumption and avoidable congestion, even leading to higher overall and individual travel times. In contrast, strategic routing aims at optimizing the traffic for all agents regarding a global optimization goal. We introduce a framework to formalize real-world strategic routing scenarios as algorithmic problems and study one of them, which we call Single Alternative Path (SAP), in detail. There, we are given an original route between a single origin-destination pair. The goal is to suggest an alternative route to all agents that optimizes the overall travel time under the assumption that the agents distribute among both routes according to a psychological model, for which we introduce the concept of Pareto-conformity. We show that the SAP problem is NP-complete, even for such models. Nonetheless, assuming Pareto-conformity, we give multiple algorithms for different variants of SAP, using multi-criteria shortest path algorithms as subroutines. Moreover, we prove that several natural models are in fact Pareto-conform. The implementation and evaluation of our algorithms serve as a proof of concept, showing that SAP can be solved in reasonable time even though the algorithms have exponential running time in the worst case
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