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Fernando Santos

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5 papers

1 author row

AAAI Conference 2018 Conference Paper

Engineering Pro-Sociality With Autonomous Agents

Ana Paiva
Fernando Santos
Francisco Santos

This paper envisions a future where autonomous agents are used to foster and support pro-social behavior in a hybrid society of humans and machines. Pro-social behavior occurs when people and agents perform costly actions that beneﬁt others. Acts such as helping others voluntarily, donating to charity, providing informations or sharing resources, are all forms of pro-social behavior. We discuss two questions that challenge a purely utilitarian view of human decision making and contextualize its role in hybrid societies: i) What are the conditions and mechanisms that lead societies of agents and humans to be more pro-social? ii) How can we engineer autonomous entities (agents and robots) that lead to more altruistic and cooperative behaviors in a hybrid society? We propose using social simulations, game theory, population dynamics, and studies with people in virtual or real environments (with robots) where both agents and humans interact. This research will constitute the basis for establishing the foundations for the new ﬁeld of Pro-social Computing, aiming at understanding, predicting and promoting pro-sociality among humans, through artiﬁcial agents and multiagent systems.

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AAAI Conference 2018 Short Paper

Indirect Reciprocity and Costly Assessment in Multiagent Systems

Fernando Santos
Jorge Pacheco
Francisco Santos

Social norms can help solving cooperation dilemmas, constituting a key ingredient in systems of indirect reciprocity (IR). Under IR, agents are associated with different reputations, whose attribution depends on socially adopted norms that judge behaviors as good or bad. While the pros and cons of having a certain public image depend on how agents learn to discriminate between reputations, the mechanisms incentivizing agents to report the outcome of their interactions remain unclear, especially when reporting involves a cost (costly reputation building). Here we develop a new model – inspired in evolutionary game theory – and show that two social norms can sustain high levels of cooperation, even if reputation building is costly. For that, agents must be able to anticipate the reporting intentions of their opponents. Cooperation depends sensitively on both the cost of reporting and the accuracy level of reporting anticipation.

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AAAI Conference 2018 Conference Paper

Social Norms of Cooperation With Costly Reputation Building

Fernando Santos
Jorge Pacheco
Francisco Santos

Social norms regulate actions in artiﬁcial societies, steering collective behavior towards desirable states. In real societies, social norms can solve cooperation dilemmas, constituting a key ingredient in systems of indirect reciprocity: reputations of agents are assigned following social norms that identify their actions as good or bad. This, in turn, implies that agents can discriminate between the different actions of others and that the behaviors of each agent are known to the population at large. This is only possible if the agents report their interactions. Reporting constitutes, this way, a fundamental ingredient of indirect reciprocity, as in its absence cooperation in a multiagent system may collapse. Yet, in most studies to date, reporting is assumed to be cost-free, which collides with many life situations, where reporting can easily incur a cost (costly reputation building). Here we develop a new model of indirect reciprocity that allows reputation building to be costly. We show that only two norms can sustain cooperation under costly reputation building, a feature that requires agents to be able to anticipate the reporting intentions of their opponents, depending sensitively on both the cost of reporting and the accuracy level of reporting anticipation.

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AAMAS Conference 2017 Conference Paper

ABStractme: Modularized Environment Modeling in Agent-based Simulations

Deividi Moreira
Fernando Santos
Matheus Barbieri
Ingrid Nunes
Ana L. C. Bazzan

This paper presents ABStractme, a tool for modeling the simulated environment in agent-based simulations. Differently from existing alternatives, ABStractme allows specification of the environment in terms of concerns, which improve modularization. Moreover, it supports the modeling of setup aspects of the simulation, in addition to entities and the spatial abstraction. The tool generates ready-touse code for the NetLogo simulation platform. A user study provided evidence that ABStractme is useful, enjoyable, and easy to use and learn. Demonstration video: https: //youtu. be/Z4DeVDwdjVw

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AAMAS Conference 2017 Conference Paper

Model-Driven Engineering in Agent-based Modeling and Simulation: a Case Study in the Traffic Signal Control Domain

Fernando Santos
Ingrid Nunes
Ana L. C. Bazzan

Model-driven engineering (MDE) is an approach for improving productivity in software development. This approach was exploited in the context of agent-based modeling and simulation (ABMS) only to a certain extent. Previous work has not shown real evidence of the benefits that MDE promotes in ABMS. This paper thus explores the use of MDE in ABMS with a case study in the traffic domain. We propose a domain analysis method to identify domain concepts and a modeling language that provides building blocks for them. Our evaluation gives evidence that our MDE approach reduces the effort to develop agent-based simulations.

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