4,595 research outputs found
The representation of planning strategies
AbstractAn analysis of strategies, recognizable abstract patterns of planned behavior, highlights the difference between the assumptions that people make about their own planning processes and the representational commitments made in current automated planning systems. This article describes a project to collect and represent strategies on a large scale to identify the representational components of our commonsense understanding of intentional action. Three hundred and seventy-two strategies were collected from ten different planning domains. Each was represented in a pre-formal manner designed to reveal the assumptions that these strategies make concerning the human planning process. The contents of these representations, consisting of nearly one thousand unique concepts, were then collected and organized into forty-eight groups that outline the representational requirements of strategic planning systems
Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Review
Advocates for Neuro-Symbolic Artificial Intelligence (NeSy) assert that
combining deep learning with symbolic reasoning will lead to stronger AI than
either paradigm on its own. As successful as deep learning has been, it is
generally accepted that even our best deep learning systems are not very good
at abstract reasoning. And since reasoning is inextricably linked to language,
it makes intuitive sense that Natural Language Processing (NLP), would be a
particularly well-suited candidate for NeSy. We conduct a structured review of
studies implementing NeSy for NLP, with the aim of answering the question of
whether NeSy is indeed meeting its promises: reasoning, out-of-distribution
generalization, interpretability, learning and reasoning from small data, and
transferability to new domains. We examine the impact of knowledge
representation, such as rules and semantic networks, language structure and
relational structure, and whether implicit or explicit reasoning contributes to
higher promise scores. We find that systems where logic is compiled into the
neural network lead to the most NeSy goals being satisfied, while other factors
such as knowledge representation, or type of neural architecture do not exhibit
a clear correlation with goals being met. We find many discrepancies in how
reasoning is defined, specifically in relation to human level reasoning, which
impact decisions about model architectures and drive conclusions which are not
always consistent across studies. Hence we advocate for a more methodical
approach to the application of theories of human reasoning as well as the
development of appropriate benchmarks, which we hope can lead to a better
understanding of progress in the field. We make our data and code available on
github for further analysis.Comment: Surve
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
The Future of Cognitive Strategy-enhanced Persuasive Dialogue Agents: New Perspectives and Trends
Persuasion, as one of the crucial abilities in human communication, has
garnered extensive attention from researchers within the field of intelligent
dialogue systems. We humans tend to persuade others to change their viewpoints,
attitudes or behaviors through conversations in various scenarios (e.g.,
persuasion for social good, arguing in online platforms). Developing dialogue
agents that can persuade others to accept certain standpoints is essential to
achieving truly intelligent and anthropomorphic dialogue system. Benefiting
from the substantial progress of Large Language Models (LLMs), dialogue agents
have acquired an exceptional capability in context understanding and response
generation. However, as a typical and complicated cognitive psychological
system, persuasive dialogue agents also require knowledge from the domain of
cognitive psychology to attain a level of human-like persuasion. Consequently,
the cognitive strategy-enhanced persuasive dialogue agent (defined as
CogAgent), which incorporates cognitive strategies to achieve persuasive
targets through conversation, has become a predominant research paradigm. To
depict the research trends of CogAgent, in this paper, we first present several
fundamental cognitive psychology theories and give the formalized definition of
three typical cognitive strategies, including the persuasion strategy, the
topic path planning strategy, and the argument structure prediction strategy.
Then we propose a new system architecture by incorporating the formalized
definition to lay the foundation of CogAgent. Representative works are detailed
and investigated according to the combined cognitive strategy, followed by the
summary of authoritative benchmarks and evaluation metrics. Finally, we
summarize our insights on open issues and future directions of CogAgent for
upcoming researchers.Comment: 36 pages, 6 figure
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