Developing effective multi-agent systems (MASs) is critical for many applications requiring collaboration and coordination with humans. Despite the rapid advance of multi-agent deep reinforcement learning (MADRL) in cooperative MASs, one of the major challenges that remain is the simultaneous learning and interaction of independent agents in dynamic environments in the presence of stochastic rewards. State-of-the-art MADRL models struggle to perform well in Coordinated Multi-agent Object Transportation Problems (CMOTPs) wherein agents must coordinate with each other and learn from stochastic rewards. In contrast, humans often learn rapidly to adapt to non-stationary environments that require coordination among people. In this article, motivated by the demonstrated ability of cognitive models based on Instance-based Learning Theory (IBLT) to capture human decisions in many dynamic decision-making tasks, we propose three variants of multi-agent IBL models (MAIBLs). The idea of these MAIBL algorithms is to combine the cognitive mechanisms of IBLT and the techniques of MADRL models to deal with coordination MASs in stochastic environments from the perspective of independent learners. We demonstrate that the MAIBL models exhibit faster learning and achieve better coordination in a dynamic CMOTP task with various settings of stochastic rewards compared to current MADRL models. We discuss the benefits of integrating cognitive insights into MADRL models.

Masking strategies for cyberdefense (i.e., disguising network attributes to hide the real state of the network) are predicted to be effective in simulated experiments. However, it is unclear how effective they are against human attackers. We address three factors that challenge the effectiveness of the masking strategies in practice: (1) we relax the assumption of rationality of the attackers made by Game Theory/Machine Learning defense algorithms; (2) we provide a cognitive model of human attackers that can inform these defense algorithms; and (3) we provide a way to generate data on attacker’s decisions through simulation with a cognitive model. Two masking strategies of defense were generated using Game Theory and Machine Learning (ML) algorithms. The effectiveness of these two masking strategies of defense, risk averse and rational, are compared in an experiment with human attackers. We collected attacker’s decisions against the two masking strategies. With the limited human participant’s data, the results indicate that the risk averse strategy can reduce the defense losses compared to the rational masking strategy. We also propose a cognitive model based on Instance-Based Learning Theory that accurately represents and predicts the attacker’s decisions in this task. We demonstrate the model’s process by generating simulated data and comparing it to the attacker’s actual actions in the experiment. The model is able to capture the data at the aggregate and at the individual levels of attackers making decisions in both rational and risk averse defense algorithms. We propose that this model can be used to inform game theoretic defense algorithms and to produce synthetic data that can be used by ML algorithms to generate new defense strategies.

The trust that humans place on recommendations is key to the success of recommender systems. The formation and decay of trust in recommendations is a dynamic process influenced by context, human preferences, accuracy of recommendations, and the interactions of these factors. This paper describes two psychological experiments (N=400) that evaluate the evolution of trust in recommendations over time, under personalized and non-personalized recommendations by matching or not matching a participant's profile. Main findings include: Humans trust inaccurate recommendations more than they should; when recommendations are personalized, they lose trust in inaccurate recommendations faster than when recommendations are not personalized; and participants report less trust and lower overall ratings of personalized but inaccurate recommendations compared to not-personalized inaccurate recommendations. We make connections to the possible implications of these psychological findings to the design of recommender systems.

In the global economy, design of digital media often involves teams of individuals from a variety of cultures who must function together. Similarly, products must be designed and marketed taking specific cultural characteristics into account. Much is known about decision processes, culture and cognition, design of products and interfaces for human interaction with machines, and organizational processes, but this knowledge is dispersed across several disciplines and research areas. This article reviews current work in these areas and proposes a research agenda for fostering increased understanding of the ways in which cultural differences influence decision making and action in design and use of digital media.

Article Does animation in user interfaces improve decision making? Share on Author: Cleotilde Gonzalez Universidad de las Americas-Puebla, Computer Engineering Department, Apartado Postal 100, Cholula 72820, Puebla, Mexico Universidad de las Americas-Puebla, Computer Engineering Department, Apartado Postal 100, Cholula 72820, Puebla, MexicoView Profile Authors Info & Claims CHI '96: Proceedings of the SIGCHI Conference on Human Factors in Computing SystemsApril 1996 Pages 27–34https://doi.org/10.1145/238386.238396Online:13 April 1996Publication History 54citation1,760DownloadsMetricsTotal Citations54Total Downloads1,760Last 12 Months109Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access