Author name cluster

Steven De Jong

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

7 papers

1 author row

AAMAS Conference 2016 Conference Paper

Integrating Run-Time Incidents in a Large-Scale Simulated Urban Environment (Extended Abstract)

Steven De Jong
Alex Klein
Ruben Smelik
Freek van Wermeskerken

Motivated by many potential applications within the domain of Defense, Safety & Security, we present an approach for the injection of incidents (e. g. , an arrest following a shoplifting) in a running, large-scale urban simulation. Incidents are generated within desired user constraints such as what should happen, where, and when. We believe that this approach offers an intuitive and extendable way to create scenario-specific exceptions to daily behavior patterns.

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TIST Journal 2015 Journal Article

Metastrategies in Large-Scale Bargaining Settings

Daniel Hennes
Steven De Jong
Karl Tuyls
Ya’akov (Kobi) Gal

This article presents novel methods for representing and analyzing a special class of multiagent bargaining settings that feature multiple players, large action spaces, and a relationship among players’ goals, tasks, and resources. We show how to reduce these interactions to a set of bilateral normal-form games in which the strategy space is significantly smaller than the original settings while still preserving much of their structural relationship. The method is demonstrated using the Colored Trails (CT) framework, which encompasses a broad family of games and has been used in many past studies. We define a set of heuristics (metastrategies) in multiplayer CT games that make varying assumptions about players’ strategies, such as boundedly rational play and social preferences. We show how these CT settings can be decomposed into canonical bilateral games such as the Prisoners’ Dilemma, Stag Hunt, and Ultimatum games in a way that significantly facilitates their analysis. We demonstrate the feasibility of this approach in separate CT settings involving one-shot and repeated bargaining scenarios, which are subsequently analyzed using evolutionary game-theoretic techniques. We provide a set of necessary conditions for CT games for allowing this decomposition. Our results have significance for multiagent systems researchers in mapping large multiplayer CT task settings to smaller, well-known bilateral normal-form games while preserving some of the structure of the original setting.

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AAMAS Conference 2011 Conference Paper

Bee-Inspired Foraging In An Embodied Swarm

Sjriek Alers
Daan Bloembergen
Daniel Hennes
Steven De Jong
Michael Kaisers
Nyree Lemmens
Karl Tuyls
Gerhard Weiss

We show the emergence of Swarm Intelligence in physical robots. We transfer an optimization algorithm which is based on beeforaging behavior to a robotic swarm. In simulation this algorithm has already been shown to be more effective, scalable and adaptive than algorithms inspired by ant foraging. In addition to this advantage, bee-inspired foraging does not require (de-)centralized simulation of environmental parameters (e. g. pheromones).

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AAMAS Conference 2011 Conference Paper

Metastrategies in the Colored Trails Game

Steven De Jong
Daniel Hennes
Karl Tuyls
Ya'akov (Kobi) Gal

This paper presents a novel method to describe and analyze strategic interactions in settings that include multiple actors, many possible actions and relationships among goals, tasks and resources. It shows how to reduce these large interactions to a set of bilateral normal-form games in which the strategy space is significantly smaller than the original setting, while still preserving many of its strategic characteristics. We demonstrate this technique on the Colored Trails (CT) framework, which encompasses a broad family of games defining multi-agent interactions and has been used in many past studies. We define a set of representative heuristics in a three-player CT setting. Choosing players' strategies from this set, the original CT setting is analytically decomposed into canonical bilateral social dilemmas, i. e. , Prisoners' Dilemma, Stag Hunt and Ultimatum games. We present a set of criteria for generating strategically interesting CT games and empirically show that they indeed decompose into bilateral social dilemmas if players play according to the heuristics. Our results have significance for multi-agent systems researchers in mapping large multi-player task settings to well-known bilateral normal-form games in a way that facilitates the analysis of the original setting.

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JAAMAS Journal 2010 Journal Article

Human-inspired computational fairness

Steven De Jong
Karl Tuyls

Abstract In many common tasks for multi-agent systems, assuming individually rational agents leads to inferior solutions. Numerous researchers found that fairness needs to be considered in addition to individual reward, and proposed valuable computational models of fairness. In this paper, we argue that there are two opportunities for improvement. First, existing models are not specifically tailored to addressing a class of tasks named social dilemmas, even though such tasks are quite common in the context of multi-agent systems. Second, the models generally rely on the assumption that all agents will and can adhere to these models, which is not always the case. We therefore present a novel computational model, i. e. , human-inspired computational fairness. Upon being confronted with social dilemmas, humans may apply a number of fully decentralized sanctioning mechanisms to ensure that optimal, fair solutions emerge, even though some participants may be deciding purely on the basis of individual reward. In this paper, we show how these human mechanisms may be computationally modelled, such that fair and optimal solutions emerge from agents being confronted with social dilemmas.

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AAMAS Conference 2008 Conference Paper

Artificial agents learning human fairness

Steven De Jong
Karl Tuyls
Katja Verbeeck

Recent advances in technology allow multi-agent systems to be deployed in cooperation with or as a service for humans. Typically, those systems are designed assuming individually rational agents, according to the principles of classical game theory. However, research in the field of behavioral economics has shown that humans are not purely self-interested: they strongly care about fairness. Therefore, multi-agent systems that fail to take fairness into account, may not be sufficiently aligned with human expectations and may not reach intended goals. In this paper, we present a computational model for achieving fairness in adaptive multi-agent systems. The model uses a combination of Continuous Action Learning Automata and the Homo Egualis utility function. The novel contribution of our work is that this function is used in an explicit, computational manner. We show that results obtained by agents using this model are compatible with experimental and analytical results on human fairness, obtained in the field of behavioral economics.

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KER Journal 2008 Journal Article

Fairness in multi-agent systems

Steven De Jong
Karl Tuyls
Katja Verbeeck

Abstract Multi-agent systems are complex systems in which multiple autonomous entities, called agents, cooperate in order to achieve a common or personal goal. These entities may be computer software, robots, and also humans. In fact, many multi-agent systems are intended to operate in cooperation with or as a service for humans. Typically, multi-agent systems are designed assuming perfectly rational, self-interested agents, according to the principles of classical game theory. Recently, such strong assumptions have been relaxed in various ways. One such way is explicitly including principles derived from human behavior. For instance, research in the field of behavioral economics shows that humans are not purely self-interested. In addition, they strongly care about fairness. Therefore, multi-agent systems that fail to take fairness into account, may not be sufficiently aligned with human expectations and may not reach intended goals. In this paper, we present an overview of work in the area of fairness in multi-agent systems. More precisely, we first look at the classical agent model, that is, rational decision making. We then provide an outline of descriptive models of fairness, that is, models that explain how and why humans reach fair decisions. Then, we look at prescriptive, computational models for achieving fairness in adaptive multi-agent systems. We show that results obtained by these models are compatible with experimental and analytical results obtained in the field of behavioral economics.

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