New York Conferences 2023 - International Conference on Artificial Intelligence
September 1-3, 2023
New York City

Submission Guidelines

We accept files in .docx/.doc/.pdf/Latex Pdf format - as per the call for papers schedule.
Papers should be thoroughly checked and proofread before submission. After you have submitted your article you are unable to make any changes to it during the refereeing process—although if accepted, you will have a chance to make minor revisions after refereeing and before the final submission of your paper.
Formatting Guidelines

Paper content must be original and relevant to one of the many conference topics.
Authors are required to ensure accuracy of quotations, citations, diagrams, maps, and tables.
Figures and tables need to be placed where they are to appear in the text and must be clear and easy to view.
Papers must follow format according to the template which will be sent to accepted 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     Ranking     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     Q-learning     Policy iteration     Temporal difference learning     Approximate dynamic programming methods     Ensemble methods     Boosting     Bagging     Spectral methods     Feature selection     Regularization     Cross-validation