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人教版高中英语新课程教材中,语言运用（Using Language）是每个单元必不可少的部分,提供了围绕单元中心话题的听、说、读、写的综合性练习,是单元中心话题的延续和升华.如何设计Using Language部分的教学,使自己的教学模式既不落俗套,又能真正体现新课程标准所倡导的教学理念,正是广大一线英语教师一直努力探索的问题.

打磨Using Language,倡导新理念

Research into Explainable Artificial Intelligence (XAI) has been increasing in recent years as a response to the need for increased transparency and trust in AI. This is particularly important as AI is used in sensitive domains with societal, ethical, and safety implications. Work in XAI has primarily focused on Machine Learning (ML) for classification, decision, or action, with detailed systematic reviews already undertaken. This review looks to explore current approaches and limitations for XAI in the area of Reinforcement Learning (RL). From 520 search results, 25 studies (including 5 snowball sampled) are reviewed, highlighting visualization, query-based explanations, policy summarization, human-in-the-loop collaboration, and verification as trends in this area. Limitations in the studies are presented, particularly a lack of user studies, and the prevalence of toy-examples and difficulties providing understandable explanations. Areas for future study are identified, including immersive visualization, and symbolic representation.

/pdf/explainable-ai-and-reinforcement-learning-a-systematic-2inoawm0xh.pdf

Explainable AI and Reinforcement Learning-A Systematic Review of Current Approaches and Trends.

On the origin of the human mind.

Knowledge Representation, Reasoning, and the Design of Intelligent Agents: Creating a Knowledge Base

This paper describes an integrated architecture for representing, reasoning with, and interactively learning domain knowledge in the context of human-robot collaboration. Specifically, Answer Set Prolog, a declarative language, is used to represent and reason with incomplete commonsense knowledge about the domain. Non-monotonic logical reasoning identifies knowledge gaps and guides the interactive learning of relations that represent actions, and of axioms that encode affordances and action preconditions and effects. Learning uses probabilistic models of uncertainty, and observations from active exploration, reactive action execution, and human (verbal) descriptions. The learned actions and axioms are used for subsequent reasoning. The architecture is evaluated on a simulated robot assisting humans in an indoor domain.

Knowledge representation and interactive learning of domain knowledge for human-robot interaction

In this paper we present a computational approach to key aspects of understanding social interactions. First, we specify a class of problems – understanding fables – that require inference about agents’ mental states from their behavior. After this, we review earlier work on UMBRA, an abductive system for single-agent plan understanding, and describe extensions that let it deal with multi-agent scenarios, including ones that involve accidental errors and intentional deceptions. These augmentations include distinguishing domain-level knowledge from more general content about social interactions and applying this knowledge at nested levels of belief. We also report the results of experimental studies on a set of fable-based scenarios that demonstrate the benefits of these extensions. In closing, we discuss how our approach to social cognition is informed by earlier research in the area. “Oh, what a tangled web we weave / When first we practise to deceive!” – Sir Walter Scott (Marmion, 1808)

An Abductive Approach to Understanding Social Interactions

There is general agreement that knowledge plays a key role in intelligent behavior, but most work on this topic has emphasized domain-specific expertise. We argue, in contrast, that cognitive systems also benefit from meta-level knowledge that has a domain-independent character. In this paper, we propose a representational framework that distinguishes between these two forms of content, along with an integrated architecture that supports their use for abductive interpretation and hierarchical skill execution. We demonstrate this framework’s viability on high-level aspects of extended dialogue that require reasoning about, and altering, participants’ beliefs and goals. Furthermore, we demonstrate its generality by showing that the meta-level knowledge operates with different domain-level content. We conclude by reviewing related work on these topics and discussing promising directions for future research.

/pdf/integrating-meta-level-and-domain-level-knowledge-for-2mr0qn6c9v.pdf

Integrating Meta-Level and Domain-Level Knowledge for Interpretation and Generation of Task-Oriented Dialogue

In this paper we present a computational approach to key aspects of understanding social interactions. First, we specify a class of problems ‐ understanding fables ‐ that require inference about agents’ mental states from their behavior. After this, we review earlier work on UMBRA, an abductive system for single-agent plan understanding, and describe extensions that let it deal with multiagent scenarios, including ones that involve accidental errors and intentional deceptions. These augmentations include distinguishing domain-level knowledge from more general content about social interactions and applying this knowledge at nested levels of belief. We also report results on a set of fable-based scenarios that demonstrate the benefits of these extensions. In closing, we discuss how our approach to social cognition is informed by earlier research in the area.

/pdf/understanding-social-interactions-using-incremental-3e9zt1anmw.pdf

Understanding Social Interactions Using Incremental Abductive Inference

https://www.mdpi.com/2218-6581/5/4/21/pdf

Towards an Explanation Generation System for Robots: Analysis and Recommendations

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