Those designing autonomous systems that interact with humans will invariably
face questions about how humans think and make decisions. Fortunately,
computational cognitive science offers insight into human decision-making using
tools that will be familiar to those with backgrounds in optimization and
control (e.g., probability theory, statistical machine learning, and
reinforcement learning). Here, we review some of this work, focusing on how
cognitive science can provide forward models of human decision-making and
inverse models of how humans think about others' decision-making. We highlight
relevant recent developments, including approaches that synthesize blackbox and
theory-driven modeling, accounts that recast heuristics and biases as forms of
bounded optimality, and models that characterize human theory of mind and
communication in decision-theoretic terms. In doing so, we aim to provide
readers with a glimpse of the range of frameworks, methodologies, and
actionable insights that lie at the intersection of cognitive science and
control research.

在设计与人交互的自主系统时，计算认知科学提供了人类决策制定背后的洞见，聚焦于如何提供人类决策的前向模型和如何理解人类对他人决策的反向模型，并回顾了近期相关进展，提供了框架、方法论和可行性洞见。

认知科学作为人类决策的正向和反向模型之源，用于机器人技术和控制

Cognitive science as a source of forward and inverse models of human  decisions for robotics and control

We present an inductive spatio-temporal learning framework rooted in
inductive logic programming. With an emphasis on visuo-spatial language, logic,
and cognition, the framework supports learning with relational spatio-temporal
features identifiable in a range of domains involving the processing and
interpretation of dynamic visuo-spatial imagery. We present a prototypical
system, and an example application in the domain of computing for visual arts
and computational cognitive science.

研究了一种基于归纳逻辑规划的归纳时空学习框架，支持包含处理和解释动态视觉空间图像的多个领域中，通过关系时空特征的学习。提供了一个原型系统和一个在视觉艺术和计算认知科学领域的示例应用。

深度语义归纳时空学习

Deeply Semantic Inductive Spatio-Temporal Learning

In computational cognitive science, the cognitive architecture ACT-R is very
popular. It describes a model of cognition that is amenable to computer
implementation, paving the way for computational psychology. Its underlying
psychological theory has been investigated in many psychological experiments,
but ACT-R lacks a formal definition of its underlying concepts from a
mathematical-computational point of view. Although the canonical implementation
of ACT-R is now modularized, this production rule system is still hard to adapt
and extend in central components like the conflict resolution mechanism (which
decides which of the applicable rules to apply next).
In this work, we present a concise implementation of ACT-R based on
Constraint Handling Rules which has been derived from a formalization in prior
work. To show the adaptability of our approach, we implement several different
conflict resolution mechanisms discussed in the ACT-R literature. This results
in the first implementation of one such mechanism. For the other mechanisms, we
empirically evaluate if our implementation matches the results of reference
implementations of ACT-R.

本文介绍了一种基于约束处理规则的 ACT-R 认知架构的简洁实现，并实现了 ACT-R 中多个不同的冲突解决机制，是第一个实现其中一种机制的文章，文章还对比了其他机制的参考实现的结果。