Titlebook: Deceptive AI; First International Stefan Sarkadi,Benjamin Wright,Peter McBurney Conference proceedings 2021 Springer Nature Switzerland AG

ALLEY

Location of Bases for Prehospital Servicesleep hygiene. This paper introduces the preliminary results of an ongoing study and two types of deceptions within the framework of nudging: self-deception by cognitive biases and deception by non-transparency.

厌恶

Kamini,Shalli Rani,Ali Kashif Bashirure as well as behaviors for the deceiving team (the shills and lead agent). These behaviors were implemented and simulations were run for a variety of conditions. The results show how shills can facilitate misdirection in certain circumstances. They provide insights into enhancing multi-robot team deception.

verdict

ethnology

Wolves in Sheep’s Clothing: Using Shill Agents to Misdirect Multi-robot Teamsure as well as behaviors for the deceiving team (the shills and lead agent). These behaviors were implemented and simulations were run for a variety of conditions. The results show how shills can facilitate misdirection in certain circumstances. They provide insights into enhancing multi-robot team deception.

肉身

垄断

The Role of Environments in Affording Deceptive Behaviour: Some Preliminary Insights from Stage Magiceptive agents but also the nature of the environments in which they act. To illustrate this approach, we report work-in-progress to design a game called ., played between a software agent and a human opponent, that is informed by the principles of stage magic to embed deceptive possibilities into i

muster

阻止

Wolves in Sheep’s Clothing: Using Shill Agents to Misdirect Multi-robot Teamsetition), they are at risk for being deceived by competitors. Competitors can misdirect the team to gain a positional advantage. Our lab is exploring ways in which teams of robots can be misdirected, in part, so counter-deception strategies can be devised. This paper explores how robot shills can be

我悲伤

Learning to Deceive in Multi-agent Hidden Role Gamesto simplistic settings, restricting their applicability to complex real-world problems. This paper addresses this by introducing a new mixed competitive-cooperative multi-agent reinforcement learning (MARL) environment, inspired by popular role-based deception games such as Werewolf, Avalon, and Amo

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