30,527 research outputs found
From Statistical Relational to Neurosymbolic Artificial Intelligence: a Survey
This survey explores the integration of learning and reasoning in two
different fields of artificial intelligence: neurosymbolic and statistical
relational artificial intelligence. Neurosymbolic artificial intelligence
(NeSy) studies the integration of symbolic reasoning and neural networks, while
statistical relational artificial intelligence (StarAI) focuses on integrating
logic with probabilistic graphical models. This survey identifies seven shared
dimensions between these two subfields of AI. These dimensions can be used to
characterize different NeSy and StarAI systems. They are concerned with (1) the
approach to logical inference, whether model or proof-based; (2) the syntax of
the used logical theories; (3) the logical semantics of the systems and their
extensions to facilitate learning; (4) the scope of learning, encompassing
either parameter or structure learning; (5) the presence of symbolic and
subsymbolic representations; (6) the degree to which systems capture the
original logic, probabilistic, and neural paradigms; and (7) the classes of
learning tasks the systems are applied to. By positioning various NeSy and
StarAI systems along these dimensions and pointing out similarities and
differences between them, this survey contributes fundamental concepts for
understanding the integration of learning and reasoning.Comment: To appear in Artificial Intelligence. Shorter version at IJCAI 2020
survey track, https://www.ijcai.org/proceedings/2020/0688.pd
Learning and Reasoning for Robot Sequential Decision Making under Uncertainty
Robots frequently face complex tasks that require more than one action, where
sequential decision-making (SDM) capabilities become necessary. The key
contribution of this work is a robot SDM framework, called LCORPP, that
supports the simultaneous capabilities of supervised learning for passive state
estimation, automated reasoning with declarative human knowledge, and planning
under uncertainty toward achieving long-term goals. In particular, we use a
hybrid reasoning paradigm to refine the state estimator, and provide
informative priors for the probabilistic planner. In experiments, a mobile
robot is tasked with estimating human intentions using their motion
trajectories, declarative contextual knowledge, and human-robot interaction
(dialog-based and motion-based). Results suggest that, in efficiency and
accuracy, our framework performs better than its no-learning and no-reasoning
counterparts in office environment.Comment: In proceedings of 34th AAAI conference on Artificial Intelligence,
202
The VEX-93 environment as a hybrid tool for developing knowledge systems with different problem solving techniques
The paper describes VEX-93 as a hybrid environment for developing
knowledge-based and problem solver systems. It integrates methods and
techniques from artificial intelligence, image and signal processing and
data analysis, which can be mixed. Two hierarchical levels of reasoning
contains an intelligent toolbox with one upper strategic inference engine
and four lower ones containing specific reasoning models: truth-functional
(rule-based), probabilistic (causal networks), fuzzy (rule-based) and
case-based (frames). There are image/signal processing-analysis capabilities
in the form of programming languages with more than one hundred primitive
functions.
User-made programs are embeddable within knowledge basis, allowing the
combination of perception and reasoning. The data analyzer toolbox contains
a collection of numerical classification, pattern recognition and ordination
methods, with neural network tools and a data base query language at
inference engines's disposal.
VEX-93 is an open system able to communicate with external computer programs
relevant to a particular application. Metaknowledge can be used for
elaborate conclusions, and man-machine interaction includes, besides windows
and graphical interfaces, acceptance of voice commands and production of
speech output.
The system was conceived for real-world applications in general domains, but
an example of a concrete medical diagnostic support system at present under
completion as a cuban-spanish project is mentioned.
Present version of VEX-93 is a huge system composed by about one and half
millions of lines of C code and runs in microcomputers under Windows 3.1.Postprint (published version
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