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NYC-2024-AI 2024 : New York Annual Conference on Artificial Intelligence 2024


When Jun 27, 2024 - Jun 29, 2024
Where New York, USA
Submission Deadline Jan 20, 2024
Notification Due Feb 10, 2024
Final Version Due Apr 30, 2024

Call For Papers

Topics of interest for submission include but are not limited to:
Natural language processing

Information extraction
Machine translation
Discourse, dialogue and pragmatics
Natural language generation
Speech recognition
Lexical semantics
Phonology / morphology
Language resources
Knowledge representation and reasoning

Description logics
Semantic networks
Nonmonotonic, default reasoning and belief revision
Probabilistic reasoning
Vagueness and fuzzy logic
Causal reasoning and diagnostics
Temporal reasoning
Cognitive robotics
Ontology engineering
Logic programming and answer set programming
Spatial and physical reasoning
Reasoning about belief and knowledge
Planning and scheduling

Planning for deterministic actions
Planning under uncertainty
Multi-agent planning
Planning with abstraction and generalization
Robotic planning
Evolutionary robotics
Search methodologies

Heuristic function construction
Discrete space search
Continuous space search
Randomized search
Game tree search
Abstraction and micro-operators
Search with partial observations
Control methods

Robotic planning
Evolutionary robotics
Computational control theory
Motion path planning
Philosophical/theoretical foundations of artificial intelligence

Cognitive science
Theory of mind
Distributed artificial intelligence

Multi-agent systems
Intelligent agents
Mobile agents
Cooperation and coordination
Computer vision

Computer vision tasks
Image and video acquisition
Computer vision representations
Computer vision problems
Machine learning

Supervised learning
Supervised learning by classification
Supervised learning by regression
Structured outputs
Cost-sensitive learning
Unsupervised learning
Cluster analysis
Anomaly detection
Mixture modeling
Topic modeling
Source separation
Motif discovery
Dimensionality reduction and manifold learning
Reinforcement learning
Sequential decision making
Inverse reinforcement learning
Apprenticeship learning
Multi-agent reinforcement learning
Adversarial learning
Multi-task learning
Transfer learning
Lifelong machine learning
Learning under covariate shift
Learning settings
Batch learning
Online learning settings
Learning from demonstrations
Learning from critiques
Learning from implicit feedback
Active learning settings
Semi-supervised learning settings
Machine learning approaches
Classification and regression trees
Kernel methods
Support vector machines
Gaussian processes
Neural networks
Logical and relational learning
Inductive logic learning
Statistical relational learning
Learning in probabilistic graphical models
Maximum likelihood modeling
Maximum entropy modeling
Maximum a posteriori modeling
Mixture models
Latent variable models
Bayesian network models
Learning linear models
Perceptron algorithm
Factorization methods
Non-negative matrix factorization
Factor analysis
Principal component analysis
Canonical correlation analysis
Latent Dirichlet allocation
Rule learning
Instance-based learning
Markov decision processes
Partially-observable Markov decision processes
Stochastic games
Learning latent representations
Deep belief networks
Bio-inspired approaches
Artificial life
Evolvable hardware
Genetic algorithms
Genetic programming
Evolutionary robotics
Generative and developmental approaches
Machine learning algorithms
Dynamic programming for Markov decision processes
Value iteration
Policy iteration
Temporal difference learning
Approximate dynamic programming methods
Ensemble methods
Spectral methods
Feature selection

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