Humans and machines interact more frequently than ever and our societies are
becoming increasingly hybrid. A consequence of this hybridisation is the
degradation of societal trust due to the prevalence of AI-enabled deception.
Yet, despite our understanding of the role of trust in AI in the recent years,
we still do not have a computational theory to be able to fully understand and
explain the role deception plays in this context. This is a problem because
while our ability to explain deception in hybrid societies is delayed, the
design of AI agents may keep advancing towards fully autonomous deceptive
machines, which would pose new challenges to dealing with deception. In this
paper we build a timely and meaningful interdisciplinary perspective on
deceptive AI and reinforce a 20 year old socio-cognitive perspective on trust
and deception, by proposing the development of DAMAS -- a holistic Multi-Agent
Systems (MAS) framework for the socio-cognitive modelling and analysis of
deception. In a nutshell this paper covers the topic of modelling and
explaining deception using AI approaches from the perspectives of Computer
Science, Philosophy, Psychology, Ethics, and Intelligence Analysis.

本文提出了一个综合的多智能体系统（MAS）框架 ——DAMAS，用于进行社会认知模型和分析以及建模和解释使用人工智能方法的欺骗行为，并从计算机科学、哲学、心理学、伦理学和情报分析的角度阐述了混合化、社会信任、AI - 支持的欺骗、计算理论和欺骗性人工智能的主题。

人工智能辅助的欺骗分析：跨学科视角

Deception Analysis with Artificial Intelligence: An Interdisciplinary  Perspective

Evidence-based reasoning is at the core of many problem-solving and
decision-making tasks in a wide variety of domains. Generalizing from the
research and development of cognitive agents in several such domains, this
paper presents progress toward a computational theory for the development of
instructable cognitive agents for evidence-based reasoning tasks. The paper
also illustrates the application of this theory to the development of four
prototype cognitive agents in domains that are critical to the government and
the public sector. Two agents function as cognitive assistants, one in
intelligence analysis, and the other in science education. The other two agents
operate autonomously, one in cybersecurity and the other in intelligence,
surveillance, and reconnaissance. The paper concludes with the directions of
future research on the proposed computational theory.

本文介绍了关于证据推理认知代理的计算理论，并将其应用于政府和公共部门的关键领域。该理论通过开发四种不同类型的认知代理来验证其可行性，并探讨未来研究的方向。

面向可指导认知机器人的基于证据的推理的计算理论

Toward a Computational Theory of Evidence-Based Reasoning for  Instructable Cognitive Agents

Encoding and decoding models are widely used in systems, cognitive, and
computational neuroscience to make sense of brain-activity data. However, the
interpretation of their results requires care. Decoding models can help reveal
whether particular information is present in a brain region in a format the
decoder can exploit. Encoding models make comprehensive predictions about
representational spaces. In the context of sensory systems, encoding models
enable us to test and compare brain-computational models, and thus directly
constrain computational theory. Encoding and decoding models typically include
fitted linear-model components. Sometimes the weights of the fitted linear
combinations are interpreted as reflecting, in an encoding model, the
contribution of different sensory features to the representation or, in a
decoding model, the contribution of different measured brain responses to a
decoded feature. Such interpretations can be problematic when the predictor
variables or their noise components are correlated and when priors (or
penalties) are used to regularize the fit. Encoding and decoding models are
evaluated in terms of their generalization performance. The correct
interpretation depends on the level of generalization a model achieves (e.g. to
new response measurements for the same stimuli, to new stimuli from the same
population, or to stimuli from a different population). Significant decoding or
encoding performance of a single model (at whatever level of generality) does
not provide strong constraints for theory. Many models must be tested and
inferentially compared for analyses to drive theoretical progress.

本文探讨编码模型和解码模型在神经科学中的应用以及它们在代表空间预测、特征贡献等方面的问题。同时，文章也指出单一模型的表现无法为理论提供足够的支撑，需要比较其他模型才能推动理论进展。

解释编码和解码模型

Interpreting Encoding and Decoding Models

According to Dennett, the same system may be described using a `physical'
(mechanical) explanatory stance, or using an `intentional' (belief- and
goal-based) explanatory stance. Humans tend to find the physical stance more
helpful for certain systems, such as planets orbiting a star, and the
intentional stance for others, such as living animals. We define a formal
counterpart of physical and intentional stances within computational theory: a
description of a system as either a device, or an agent, with the key
difference being that `devices' are directly described in terms of an
input-output mapping, while `agents' are described in terms of the function
they optimise. Bayes' rule can then be applied to calculate the subjective
probability of a system being a device or an agent, based only on its
behaviour. We illustrate this using the trajectories of an object in a toy
grid-world domain.

该论文讨论了以物理立场和意图立场描述系统的形式对应于计算理论中设备和代理的概念，并通过贝叶斯规则计算系统是设备还是代理的主观概率。