711 research outputs found
SDDs are Exponentially More Succinct than OBDDs
Introduced by Darwiche (2011), sentential decision diagrams (SDDs) are
essentially as tractable as ordered binary decision diagrams (OBDDs), but tend
to be more succinct in practice. This makes SDDs a prominent representation
language, with many applications in artificial intelligence and knowledge
compilation. We prove that SDDs are more succinct than OBDDs also in theory, by
constructing a family of boolean functions where each member has polynomial SDD
size but exponential OBDD size. This exponential separation improves a
quasipolynomial separation recently established by Razgon (2013), and settles
an open problem in knowledge compilation
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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
Contextual Dictionary Lookup for Knowledge Graph Completion
Knowledge graph completion (KGC) aims to solve the incompleteness of
knowledge graphs (KGs) by predicting missing links from known triples, numbers
of knowledge graph embedding (KGE) models have been proposed to perform KGC by
learning embeddings. Nevertheless, most existing embedding models map each
relation into a unique vector, overlooking the specific fine-grained semantics
of them under different entities. Additionally, the few available fine-grained
semantic models rely on clustering algorithms, resulting in limited performance
and applicability due to the cumbersome two-stage training process. In this
paper, we present a novel method utilizing contextual dictionary lookup,
enabling conventional embedding models to learn fine-grained semantics of
relations in an end-to-end manner. More specifically, we represent each
relation using a dictionary that contains multiple latent semantics. The
composition of a given entity and the dictionary's central semantics serves as
the context for generating a lookup, thus determining the fine-grained
semantics of the relation adaptively. The proposed loss function optimizes both
the central and fine-grained semantics simultaneously to ensure their semantic
consistency. Besides, we introduce two metrics to assess the validity and
accuracy of the dictionary lookup operation. We extend several KGE models with
the method, resulting in substantial performance improvements on widely-used
benchmark datasets
Multi-Goal Multi-Agent Path Finding via Decoupled and Integrated Goal Vertex Ordering
We introduce multi-goal multi agent path finding (MAPF) which
generalizes the standard discrete multi-agent path finding (MAPF) problem.
While the task in MAPF is to navigate agents in an undirected graph from their
starting vertices to one individual goal vertex per agent, MAPF assigns
each agent multiple goal vertices and the task is to visit each of them at
least once. Solving MAPF not only requires finding collision free paths
for individual agents but also determining the order of visiting agent's goal
vertices so that common objectives like the sum-of-costs are optimized. We
suggest two novel algorithms using different paradigms to address MAPF:
a heuristic search-based search algorithm called Hamiltonian-CBS (HCBS) and a
compilation-based algorithm built using the SMT paradigm, called
SMT-Hamiltonian-CBS (SMT-HCBS). Experimental comparison suggests limitations of
compilation-based approach
Multilingual Knowledge Base Completion by Cross-lingual Semantic Relation Inference
International audienceIn the present paper, we propose a simple en-dogenous method for enhancing a multilingual knowledge base through the cross-lingual semantic relation inference. It can be run on multilingual resources prior to semantic representation learning. Multilingual knowledge bases may integrate preexisting structured resources available for resource-rich languages. We aim at performing cross-lingual inference on them to improve the low resource language by creating semantic relationships
A Call for Standardization and Validation of Text Style Transfer Evaluation
Text Style Transfer (TST) evaluation is, in practice, inconsistent.
Therefore, we conduct a meta-analysis on human and automated TST evaluation and
experimentation that thoroughly examines existing literature in the field. The
meta-analysis reveals a substantial standardization gap in human and automated
evaluation. In addition, we also find a validation gap: only few automated
metrics have been validated using human experiments. To this end, we thoroughly
scrutinize both the standardization and validation gap and reveal the resulting
pitfalls. This work also paves the way to close the standardization and
validation gap in TST evaluation by calling out requirements to be met by
future research.Comment: Accepted to Findings of ACL 202
Ask, and shall you receive?: Understanding Desire Fulfillment in Natural Language Text
The ability to comprehend wishes or desires and their fulfillment is
important to Natural Language Understanding. This paper introduces the task of
identifying if a desire expressed by a subject in a given short piece of text
was fulfilled. We propose various unstructured and structured models that
capture fulfillment cues such as the subject's emotional state and actions. Our
experiments with two different datasets demonstrate the importance of
understanding the narrative and discourse structure to address this task
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