The intent of control argumentation frameworks is to specifically model
strategic scenarios from the perspective of an agent by extending the standard
model of argumentation framework in a way that takes unquantified uncertainty
regarding arguments and attacks into account. They do not, however, adequately
account for coalition formation and interactions among a set of agents in an
uncertain environment. To address this challenge, we propose a formalism of a
multi-agent scenario via cooperation logic and investigate agents' strategies
and actions in a dynamic environment.

本文提出了一种多智能体情境下协作逻辑的形式化机制来解决在不确定环境中代理商联合和互动的挑战以及研究代理商的策略和行动。

利用合作逻辑对控制参数进行建模

Modelling Control Arguments via Cooperation Logic in Unforeseen Scenarios

Conventional Neural Machine Translation (NMT) models benefit from the
training with an additional agent, e.g., dual learning, and bidirectional
decoding with one agent decoding from left to right and the other decoding in
the opposite direction. In this paper, we extend the training framework to the
multi-agent scenario by introducing diverse agents in an interactive updating
process. At training time, each agent learns advanced knowledge from others,
and they work together to improve translation quality. Experimental results on
NIST Chinese-English, IWSLT 2014 German-English, WMT 2014 English-German and
large-scale Chinese-English translation tasks indicate that our approach
achieves absolute improvements over the strong baseline systems and shows
competitive performance on all tasks.

本文提出一种多智能体交互更新的 NMT 模型框架，包括双向解码和多种 agenets，通过互相学习提升翻译质量，实验证实该方法在多项翻译任务中具有竞争性能力。

神经机器翻译多智能体学习

Multi-agent Learning for Neural Machine Translation

Autonomous driving is a multi-agent setting where the host vehicle must apply
sophisticated negotiation skills with other road users when overtaking, giving
way, merging, taking left and right turns and while pushing ahead in
unstructured urban roadways. Since there are many possible scenarios, manually
tackling all possible cases will likely yield a too simplistic policy.
Moreover, one must balance between unexpected behavior of other
drivers/pedestrians and at the same time not to be too defensive so that normal
traffic flow is maintained.
In this paper we apply deep reinforcement learning to the problem of forming
long term driving strategies. We note that there are two major challenges that
make autonomous driving different from other robotic tasks. First, is the
necessity for ensuring functional safety - something that machine learning has
difficulty with given that performance is optimized at the level of an
expectation over many instances. Second, the Markov Decision Process model
often used in robotics is problematic in our case because of unpredictable
behavior of other agents in this multi-agent scenario. We make three
contributions in our work. First, we show how policy gradient iterations can be
used without Markovian assumptions. Second, we decompose the problem into a
composition of a Policy for Desires (which is to be learned) and trajectory
planning with hard constraints (which is not learned). The goal of Desires is
to enable comfort of driving, while hard constraints guarantees the safety of
driving. Third, we introduce a hierarchical temporal abstraction we call an
"Option Graph" with a gating mechanism that significantly reduces the effective
horizon and thereby reducing the variance of the gradient estimation even
further.

本文介绍了一种利用深度强化学习解决自动驾驶问题的方案，不同于其他机器人任务，自动驾驶需要确保功能安全和在多个智能体情境下执行正确的决策，其中的主要挑战包括如何处理多个智能体的不确定行为，以及如何在 “Desires” 策略和难以控制的路径规划之间实现平衡。