Detecting and Tracing Multi-Strategic Agents with Opponent Modelling and Bayesian Policy Reuse

2020 ◽  
Author(s):  
Hao Chen ◽  
Jian Huang ◽  
Quan Liu ◽  
Chang Wang ◽  
Hanqiang Deng
Keyword(s):  
Strategic Agents ◽  
Opponent Modelling

scholarly journals Truthful fair division without free disposal

2020 ◽  
Vol 55 (3) ◽  
pp. 523-545 ◽  
Author(s):  
Xiaohui Bei ◽  
Guangda Huzhang ◽  
Warut Suksompong
Keyword(s):  
Fair Division ◽  
Multiple Agents ◽  
Two Agents ◽  
Free Disposal ◽  
Cake Cutting ◽  
Strategic Agents ◽  
Additional Constraints

Abstract We study the problem of fairly dividing a heterogeneous resource, commonly known as cake cutting and chore division, in the presence of strategic agents. While a number of results in this setting have been established in previous works, they rely crucially on the free disposal assumption, meaning that the mechanism is allowed to throw away part of the resource at no cost. In the present work, we remove this assumption and focus on mechanisms that always allocate the entire resource. We exhibit a truthful and envy-free mechanism for cake cutting and chore division for two agents with piecewise uniform valuations, and we complement our result by showing that such a mechanism does not exist when certain additional constraints are imposed on the mechanisms. Moreover, we provide bounds on the efficiency of mechanisms satisfying various properties, and give truthful mechanisms for multiple agents with restricted classes of valuations.

scholarly journals Cake Cutting: Envy and Truth

2017 ◽  
Author(s):  
Xiaohui Bei ◽  
Ning Chen ◽  
Guangda Huzhang ◽  
Biaoshuai Tao ◽  
Jiajun Wu
Keyword(s):  
Nash Equilibrium ◽  
Private Information ◽  
General Setting ◽  
Impossibility Result ◽  
Market Model ◽  
Truth Telling ◽  
Piecewise Constant ◽  
Cake Cutting ◽  
Strategic Agents ◽  
To Receive

We study envy-free cake cutting with strategic agents, where each agent may manipulate his private information in order to receive a better allocation. We focus on piecewise constant utility functions and consider two scenarios: the general setting without any restriction on the allocations and the restricted setting where each agent has to receive a connected piece. We show that no deterministic truthful envy-free mechanism exists in the connected piece scenario, and the same impossibility result for the general setting with some additional mild assumptions on the allocations. Finally, we study a large market model where the economy is replicated and demonstrate that truth-telling converges to a Nash equilibrium.

Friends and Strategic Agents

2001 ◽  
Vol 26 (2) ◽  
pp. 124-164 ◽  
Author(s):  
Paul Olk ◽  
Marta Elvira
Keyword(s):  
Strategic Agents

Opponent Modelling, Evolution, and the Iterated Prisoner ' s Dilemma

2007 ◽  
pp. 139-170 ◽  
Author(s):  
Philip Hingston ◽  
Dan Dyer ◽  
Luigi Barone ◽  
Tim French ◽  
Graham Kendall
Keyword(s):  
Opponent Modelling

scholarly journals Determining Solution Space Characteristics for Real-Time Strategy Games and Characterizing Winning Strategies

2011 ◽  
Vol 2011 ◽  
pp. 1-17
Author(s):  
Kurt Weissgerber ◽  
Gary B. Lamont ◽  
Brett J. Borghetti ◽  
Gilbert L. Peterson
Keyword(s):  
Real Time ◽  
A Priori ◽  
Solution Space ◽  
Complex Problem ◽  
Classification Model ◽  
Landscape Characteristics ◽  
Specific Complex ◽  
Winning Strategies ◽  
Strategic Agents ◽  
Rts Game

The underlying goal of a competing agent in a discrete real-time strategy (RTS) game is to defeat an adversary. Strategic agents or participants must define an a priori plan to maneuver their resources in order to destroy the adversary and the adversary's resources as well as secure physical regions of the environment. This a priori plan can be generated by leveraging collected historical knowledge about the environment. This knowledge is then employed in the generation of a classification model for real-time decision-making in the RTS domain. The best way to generate a classification model for a complex problem domain depends on the characteristics of the solution space. An experimental method to determine solution space (search landscape) characteristics is through analysis of historical algorithm performance for solving the specific problem. We select a deterministic search technique and a stochastic search method for a priori classification model generation. These approaches are designed, implemented, and tested for a specific complex RTS game, Bos Wars. Their performance allows us to draw various conclusions about applying a competing agent in complex search landscapes associated with RTS games.

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