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Neurons and symbols: a manifesto
We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of
neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty
Image Manipulation via Multi-Hop Instructions -- A New Dataset and Weakly-Supervised Neuro-Symbolic Approach
We are interested in image manipulation via natural language text -- a task
that is useful for multiple AI applications but requires complex reasoning over
multi-modal spaces. We extend recently proposed Neuro Symbolic Concept Learning
(NSCL), which has been quite effective for the task of Visual Question
Answering (VQA), for the task of image manipulation. Our system referred to as
NeuroSIM can perform complex multi-hop reasoning over multi-object scenes and
only requires weak supervision in the form of annotated data for VQA. NeuroSIM
parses an instruction into a symbolic program, based on a Domain Specific
Language (DSL) comprising of object attributes and manipulation operations,
that guides its execution. We create a new dataset for the task, and extensive
experiments demonstrate that NeuroSIM is highly competitive with or beats SOTA
baselines that make use of supervised data for manipulation.Comment: EMNLP 2023 (long paper, main conference
Modeling of Phenomena and Dynamic Logic of Phenomena
Modeling of complex phenomena such as the mind presents tremendous
computational complexity challenges. Modeling field theory (MFT) addresses
these challenges in a non-traditional way. The main idea behind MFT is to match
levels of uncertainty of the model (also, problem or theory) with levels of
uncertainty of the evaluation criterion used to identify that model. When a
model becomes more certain, then the evaluation criterion is adjusted
dynamically to match that change to the model. This process is called the
Dynamic Logic of Phenomena (DLP) for model construction and it mimics processes
of the mind and natural evolution. This paper provides a formal description of
DLP by specifying its syntax, semantics, and reasoning system. We also outline
links between DLP and other logical approaches. Computational complexity issues
that motivate this work are presented using an example of polynomial models
Towards an Indexical Model of Situated Language Comprehension for Cognitive Agents in Physical Worlds
We propose a computational model of situated language comprehension based on
the Indexical Hypothesis that generates meaning representations by translating
amodal linguistic symbols to modal representations of beliefs, knowledge, and
experience external to the linguistic system. This Indexical Model incorporates
multiple information sources, including perceptions, domain knowledge, and
short-term and long-term experiences during comprehension. We show that
exploiting diverse information sources can alleviate ambiguities that arise
from contextual use of underspecific referring expressions and unexpressed
argument alternations of verbs. The model is being used to support linguistic
interactions in Rosie, an agent implemented in Soar that learns from
instruction.Comment: Advances in Cognitive Systems 3 (2014
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