Monday, 15 January 2018

CFP: IEEE CIM Special Issue on Deep Reinforcement Learning and Games (Oct 1)

Aims and Scope
Recently, there has been tremendous progress in artificial intelligence (AI) and computational intelligence (CI) and games. In 2015, Google DeepMind published a paper “Human-level control through deep reinforcement learning” in Nature, showing the power of AI&CI in learning to play Atari video games directly from the screen capture. Furthermore, in Nature 2016, it published a cover paper “Mastering the game of Go with deep neural networks and tree search” and proposed the computer Go program, AlphaGo. In March 2016, AlphaGo beat the world’s top Go player Lee Sedol by 4:1. In early 2017, the Master, a variant of AlphaGo, won 60 matches against top Go players. In late 2017, AlphaGo Zero learned only from self-play and was able to beat the original AlphaGo without any losses (Nature 2017). This becomes a new milestone in the AI&CI history, the core of which is the algorithm of deep reinforcement learning (DRL). Moreover, the achievements on DRL and games are manifest. In 2017, the AIs beat the expert in Texas Hold’em poker (Science 2017). OpenAI developed an AI to outperform the champion in the 1V1 Dota 2 game. Facebook released a huge database of StarCraft I. Blizzard and DeepMind turned StarCraft II into an AI research lab with a more open interface. In these games, DRL also plays an important role.

Needless to say, the great achievements of DRL are first obtained in the domain of games, and it is timely to report major advances in a special issue of IEEE Computational Intelligence MagazineIEEE Trans. on Neural network and Learning Systems and IEEE Trans. on Computational Intelligence and AI in Games have organized similar ones in 2017.

DRL is able to output control signals directly based on input images, and integrates the capacity for perception of deep learning (DL) and the decision making of reinforcement learning (RL). This mechanism has many similarities to human modes of thinking. However, there is much work left to do. The theoretical analysis of DRL, e. g., the convergence, stability, and optimality, is still in early days. Learning efficiency needs to be improved by proposing new algorithms or combining with other methods. DRL algorithms still need to be demonstrated in more diverse practical settings. Therefore, the aim of this special issue is to publish the most advanced research and state-of-the-art contributions in the field of DRL and its application in games. We expect this special issue to provide a platform for international researchers to exchange ideas and to present their latest research in relevant topics. Specific topics of interest include but are not limited to:

·       Survey on DRL and games;
·       New AI&CI algorithms in games;
·       Learning forward models from experience;
·       New algorithms of DL, RL and DRL;
·       Theoretical foundation of DL, RL and DRL;
·       DRL combined with search algorithms or other learning methods;
·       Challenges of AI&CI games as limitations in strategy learning, etc.;
·       DRL or AI&CI Games based applications in realistic and complicated systems.
Important Dates
Submission Deadline: October 1st, 2018
Notification of Review Results: December 10th, 2018
Submission of Revised Manuscripts: January 31st, 2019
Submission of Final Manuscript: March 15th, 2019
Special Issue Publication: August 2019 Issue

Guest Editors
D. Zhao, Institute of Automation, Chinese Academy of Sciences, China,

Dr. Zhao is a professor at Institute of Automation, Chinese Academy of Sciences and also a professor with the University of Chinese Academy of Sciences, China. His current research interests are in the area of deep reinforcement learning, computational intelligence, adaptive dynamic programming, games, and robotics. Dr. Zhao is the Associate Editor of IEEE Transactions on Neural Networks and Learning Systems and IEEE Computation Intelligence Magazine, etc. He is the Chair of Beijing Chapter, and the past Chair of Adaptive Dynamic Programming and Reinforcement Learning Technical Committee of IEEE Computational Intelligence Society (CIS). He works as several guest editors of renowned international journals, including the leading guest editor of the IEEE Trans.on Neural Network and Learning Systems special issue on Deep Reinforcement Learning and Adaptive Dyanmic Programming.

S. Lucas, Queen Mary University of London, UK,

Dr. Lucas was a full professor of computer science, in the School of Computer Science and Electronic Engineering at the University of Essex until July 31, 2017, and now is the Professor and Head of School of Electronic Engineering and Computer Science at Queen Mary University of London. He was the Founding Editor-in-Chief of the IEEE Transactions on Computational Intelligence and AI in Games, and also co-founded the IEEE Conference on Computational Intelligence and Games, first held at the University of Essex in 2005.  He is the Vice President for Education of the IEEE Computational Intelligence Society. His research has gravitated toward Game AI: games provide an ideal arena for AI research, and also make an excellent application area.

J. Togelius, New York University, USA,

Julian Togelius is an Associate Professor in the Department of Computer Science and Engineering, New York University, USA. He works on all aspects of computational intelligence and games and on selected topics in evolutionary computation and evolutionary reinforcement learning. His current main research directions involve search-based procedural content generation in games, general video game playing, player modeling, and fair and relevant benchmarking of AI through game-based competitions. He is the Editor-in-Chief of IEEE Transactions on Computational Intelligence and AI in Games, and a past chair of the IEEE CIS Technical Committee on Games.

Submission Instructions
1.     The IEEE CIM requires all prospective authors to submit their manuscripts in electronic format, as a PDF file. The maximum length for Papers is typically 20 double-spaced typed pages with 12-point font, including figures and references. Submitted manuscript must be typewritten in English in single column format. Authors of Papers should specify on the first page of their submitted manuscript up to 5 keywords. Additional information about submission guidelines and information for authors is provided at the IEEE CIM website. Submission will be made via
2.     Send also an email to guest editor D. Zhao ( with subject “IEEE CIM special issue submission” to notify about your submission.
3.      Early submissions are welcome. We will start the review process as soon as we receive your contribution.

CFP: IEEE TETCI Special Issue on Computational Intelligence in Data-Driven Optimization (Jan 31)


Most evolutionary algorithms and other meta-heuristic search methods typically assume that there are explicit objective functions available for fitness evaluations. In the real world, however, such explicit objective functions may not exist in many cases. For example, in many process industry optimization problems, no explicit models exist for describing the relationship between the final quality of the product and the decision variables, such as control loop outputs and grinding particle size in hematite grinding processes. Therefore, some computationally very intensive numerical simulation, such as computational fluid dynamic simulations or finite element analysis or even physical experiments, are instead conducted as the way to evaluate the fitness value. Thus, historical experimental data becomes significantly important and can be used for optimization. There are also cases where only factual data can be collected.

For solving such optimization problems, evolutionary optimization can be conducted only using a data-driven approach. Data-driven evolutionary optimization can largely be divided into two paradigms, one termed off-line data-driven optimization, where no additional data can be sampled during optimization, and the other is called on-line data-driven optimization, where only a limited number of new data points can be actively sampled during optimization. For both paradigms of data-driven optimization, seamless integration of machine learning techniques, such as model selection, ensemble learning, active learning, semi-supervised learning and transfer learning with evolutionary optimization are essential, due to the fact that data acquisition is very expensive, either computationally or costly.

This special issue aims to present the most recent advances in data-driven optimization, in particular in the integration of evolutionary algorithms and other meta-heuristic search methods with machine learning techniques, neural networks and fuzzy logic systems for surrogate modelling, data mining, preference articulation, and decision-making.


The topics of this special issue include but are not limited to the following topics:

• Surrogate-assisted optimization of computationally expensive problems
• Adaptive sampling using active learning and statistical learning techniques
• Surrogate model management in single and multiobjective optimization
• Semi-supervised and transfer learning in data driven optimization
• Machine learning for distributed data driven optimization
• Knowledge mining and transfer for data-driven optimization 
Data-driven large scale and/or many-objective optimization problems
• Preference modeling and articulation in multi- and manyobjective optimization
• Real world applications including multidisciplinary optimization


• Paper submission deadline: January 31, 2018
• Notice of the first round review: April 15, 2018
• Revision due: June 15, 2018
• Final notice of acceptance/reject: July 30, 2018


Manuscripts should be prepared according to the “Information for Authors” section of the journal ( and submissions should be done through the journal submission website:, by selecting the Manuscript Type of “Computational Intelligence in DataDriven Optimization” and clearly marking “Computational Intelligence in Data-Driven Optimization Special Issue Paper” as comments to the Editor-in-Chief. Submitted papers will be reviewed by at least three different expert reviewers. Submission of a manuscript implies that it is the authors original unpublished work and is not being submitted for possible publication elsewhere.


Dr. Chaoli Sun, Department of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan, Shanxi 030024 China. Email:

Dr. Handing Wang, Department of Computer Science, University of Surrey, Guildford, GU2 7XH, UK. Email:

Prof. Wenli Du, School of Information Science & Engineering, East China University of Science and Technology, Shanghai, 200237, China. Email:

Prof. Yaochu Jin, Department of Computer Science, University of Surrey, Guildford, GU2 7XH, UK. Email:

CFP: IEEE CIM Special Issue on Computational Intelligence for Affective Computing and Sentiment Analysis (Mar 31)


Submissions are invited for a special issue of IEEE Computational Intelligence Magazine (IEEE CIM) on Computational Intelligence for Affective Computing and Sentiment Analysis.

Emotions are intrinsically part of our mental activity and play a key role in communication and decision-making processes. Emotion is a chain of events made up of feedback loops. Feelings and behavior can affect cognition, just as cognition can influence feeling. Emotion, cognition, and action interact in feedback loops and emotion can be viewed in a structural model tied to adaptation. Besides being important for the advancement of AI, detecting and interpreting emotional information is key in multiple areas of computer science, e.g., human- agent, -computer, and -robot interaction, but also e-learning, e-health, domotics, automotive, security, user profiling and personalization.
In recent years, emotion and sentiment analysis has become increasingly popular also for processing social media data on social networks, online communities, blogs, Wikis, microblogging platforms, and other online collaborative media. The distillation of knowledge from such a big amount of unstructured information, however, is an extremely difficult task, as the contents of today's Web are perfectly suitable for human consumption, but remain hardly accessible to machines. The opportunity to capture the opinions of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences has raised growing interest both within the scientific community, leading to many exciting open challenges, as well as in the business world, due to the remarkable benefits to be had from marketing and financial market prediction.

Most of existing approaches to affective computing and sentiment analysis are still based on the syntactic representation of text, a method that relies mainly on word co-occurrence frequencies. Such algorithms are limited by the fact that they can only process information they can 'see'. As human text processors, we do not have such limitations as every word we see activates a cascade of semantically related concepts, relevant episodes, emotions, and sensory experiences, all of which enable the completion of complex NLP tasks — such as word-sense disambiguation, textual entailment, and semantic role labeling — in a quick and effortless way. Computational intelligence can aid to mimic the way humans process and analyze text and, hence, overcome the limitations of standard approaches to affective computing and sentiment analysis.

Articles are thus invited in areas such as machine learning, active learning, transfer learning, deep neural networks, neural and cognitive models, fuzzy logic, evolutionary computation, natural language processing, commonsense reasoning, and big data computing. Topics include, but are not limited to:
• Context-dependent sentiment analysis
• Deep learning for personality detection
• Deep learning for sarcasm detection
• Tensor fusion networks for sentiment analysis
• Multi-level attention networks for sentiment analysis
• Affective commonsense reasoning
• Statistical learning theory for big social data analysis
• Concept-level sentiment analysis
• Social network modeling and analysis
• Multilingual emotion and sentiment analysis
• Multimodal emotion recognition and sentiment analysis
• Aspect extraction for opinion mining
• Sentic computing
• Conceptual primitives for sentiment analysis
• Affective human-agent, -computer, and -robot interaction
• User profiling and personalization
• Time-evolving sentiment tracking

Submission Deadline: March 31st, 2018
Notification of Review Results: June 15th, 2018
Submission of Revised Manuscripts: July 15th, 2018
Submission of Final Manuscript: September 15th, 2018
Special Issue Publication: Mid-January 2019 (February 2019 Issue)

The Special Issue will consist of 3 or 4 papers on novel computational intelligence techniques for mining and analyzing emotions and opinions in text, but also in other modalities. Some papers may survey various aspects of the topic. The balance between these will be adjusted to maximize the issue's impact. All articles are expected to successfully negotiate the standard review procedures for IEEE CIM and shall be submitted via EasyChair.

• Erik Cambria, Nanyang Technological University (Singapore)
• Soujanya Poria, Nanyang Technological University (Singapore)
• Amir Hussain, University of Stirling (UK)
• Bing Liu, University of Illinois at Chicago (USA)

Saturday, 13 January 2018

Deadline extension: IEEE World Congress on Computational Intelligence (1 Feb)

Call for Papers

On behalf of the IEEE WCCI 2018 Organizing Committee, it is our great pleasure to invite you to the bi-annual IEEE World Congress on Computational Intelligence (IEEE WCCI), which is the largest technical event in the field of computational intelligence. The IEEE WCCI 2018 will host three conferences: The 2018 International Joint Conference on Neural Networks (IJCNN 2018 – co-sponsored by International Neural Network Society – INNS), the 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2018), and the 2018 IEEE Congress on Evolutionary Computation (IEEE CEC 2018) under one roof. It encourages cross-fertilization of ideas among the three big areas and provides a forum for intellectuals from all over the world to discuss and present their research findings on computational intelligence.
IEEE WCCI 2018 will be held at the Windsor Convention Centre, Rio de Janeiro, Brazil. Rio de Janeiro is one of the most attractive cities in South America, with the largest urban forest in the world, beautiful bays, lagoons and 90 kms of beaches and mountains. Known as one of the most beautiful cities in the World, Rio de Janeiro is the first city to receive the certificate of World Heritage for its Cultural Landscape. This unprecedented title was recently conferred by the United Nations Educational, Cultural and Scientific Organization (UNESCO).
Rio de Janeiro is easily accessible from all over the world, with direct flights from major cities in North America, Europe, Africa and Middle East. It is also a one stop away from Asia and Australia. The venue, The Windsor Barra Complex, features a brand new Convention Center and three different categories hotels, in the fastest growing region in Rio de Janeiro, with walking distance from a great choice of restaurants and shopping centers.
IEEE Computational Intelligence Society has maintained its position as a leader of journals in computational intelligence. CIS journals sustained their status as premier scholarly publications, earning high rankings in the Journal Citation Report by Thomson Reuters.
  • IEEE Transactions on Neural Networks and Learning Systems (IF: 4.854)
  • IEEE Transactions on Fuzzy Systems (IF: 6.701)
  • IEEE Transactions on Evolutionary Computation (IF: 5.908)
  • IEEE Computational Intelligence Magazine (IF: 3.647)

List of topics:


  • Algorithms
    • Ant colony optimization
    • Artificial immune systems
    • Coevolutionary systems
    • Cultural algorithms
    • Differential evolution
    • Estimation of distribution algorithms
    • Evolutionary programming
    • Evolution strategies
    • Genetic algorithms
    • Genetic programming
    • Heuristics, metaheuristics and hyper-heuristics
    • Interactive evolutionary computation
    • Learning classifier systems
    • Memetic, multi-meme and hybrid algorithms
    • Molecular and quantum computing
    • Multi-objective evolutionary algorithms
    • Parallel and distributed algorithms
    • Particle swarm optimization
  • Theory and Implementation
    • Adaptive dynamic programming and reinforcement learning
    • Autonomous mental development
    • Coevolution and collective behavior
    • Convergence, scalability and complexity analysis
    • Evolutionary computation theory
    • Representation and operators
    • Self-adaptation in evolutionary computation
  • Optimization
    • Numerical optimization
    • Discrete and combinatorial optimization
    • Multiobjective optimization
  • Handling of Various Aspects
    • Large-scale problems
    • Preference handling
    • Evolutionary simulation-based optimization
    • Meta-modeling and surrogate models
    • Dynamic and uncertain environments
    • Constraint and uncertainty handling
  • Hybrid Systems of Computational Intelligence
    • Evolved neural networks
    • Evolutionary fuzzy systems
    • Evolved neuro-fuzzy systems
  • Related Areas and Applications
    • Art and music
    • Artificial ecology and artificial life
    • Autonomous mental and behavior development
    • Biometrics, bioinformatics and biomedical applications
    • Classification, clustering and data analysis
    • Data mining
    • Defense and cyber security
    • Evolutionary games and multi-agent systems
    • Evolvable hardware and software
    • Evolutionary Robotics
    • Engineering applications
    • Emergent technologies
    • Finance and economics
    • Games
    • Intelligent systems applications
    • Robotics
    • Real-world applications
    • Emerging areas


    • Feedforward neural networks
    • Recurrent neural networks
    • Self-organizing maps
    • Radial basis function networks
    • Attractor neural networks and associative memory
    • Modular networks
    • Fuzzy neural networks
    • Spiking neural networks
    • Reservoir networks (echo-state networks, liquid-state machines, etc.)
    • Large-scale neural networks
    • Learning vector quantization
    • Deep neural networks
    • Randomized neural networks
    • Other topics in artificial neural networks
    • Supervised learning
    • Unsupervised learning and clustering, (including PCA, and ICA)
    • Reinforcement learning and adaptive dynamic programming
    • Semi-supervised learning
    • Online learning
    • Probabilistic and information-theoretic methods
    • Support vector machines and kernel methods
    • EM algorithms
    • Mixture models, ensemble learning, and other meta-learning or committee algorithms
    • Bayesian, belief, causal, and semantic networks
    • Statistical and pattern recognition algorithms
    • Sparse coding and models
    • Visualization of data
    • Feature selection, extraction, and aggregation
    • Evolutionary learning
    • Hybrid learning methods
    • Computational power of neural networks
    • Deep learning
    • Other topics in machine learning
    • Dynamical models of spiking neurons
    • Synchronization and temporal correlation in neural networks
    • Dynamics of neural systems
    • Chaotic neural networks
    • Dynamics of analog networks
    • Itinerant dynamics in neural systems
    • Neural oscillators and oscillator networks
    • Dynamics of attractor networks
    • Other topics in neurodynamics
    • Connectomics
    • Models of large-scale networks in the nervous system
    • Models of neurons and local circuits
    • Models of synaptic learning and synaptic dynamics
    • Models of neuromodulation
    • Brain imaging
    • Analysis of neurophysiological and neuroanatomical data
    • Cognitive neuroscience
    • Models of neural development
    • Models of neurochemical processes
    • Neuroinformatics
    • Other topics in computational neuroscience
    • Neurocognitive networks
    • Cognitive architectures
    • Models of conditioning, reward and behavior
    • Cognitive models of decision-making
    • Embodied cognition
    • Cognitive agents
    • Multi-agent models of group cognition
    • Developmental and evolutionary models of cognition
    • Visual system
    • Auditory system
    • Olfactory system
    • Other sensory systems
    • Attention
    • Learning and memory
    • Spatial cognition, representation and navigation
    • Semantic cognition and language
    • Grounding, symbol grounding
    • Neural models of symbolic processing
    • Reasoning and problem-solving
    • Working memory and cognitive control
    • Emotion and motivation
    • Motor control and action
    • Dynamical models of coordination and behavior
    • Consciousness and awareness
    • Models of sleep and diurnal rhythms
    • Mental disorders
    • Other topics in neural models of perception, cognition and action
    • Brain-machine interfaces
    • Neural prostheses
    • Neuromorphic hardware
    • Embedded neural systems
    • Other topics in neuroengineering
    • Brain-inspired cognitive architectures
    • Embodied robotics
    • Evolutionary robotics
    • Developmental robotics
    • Computational models of development
    • Collective intelligence
    • Swarms
    • Autonomous complex systems
    • Self-configuring systems
    • Self-healing systems
    • Self-aware systems
    • Emotional computation
    • Artificial life
    • Other topics in bio-inspired and biomorphic systems
    • Applications of deep neural networks
    • Bioinformatics
    • Biomedical engineering
    • Data analysis and pattern recognition
    • Speech recognition and speech production
    • Robotics
    • Neurocontrol
    • Approximate dynamic programming, adaptive critics, and Markov decision processes
    • Neural network approaches to optimization
    • Signal processing, image processing, and multi-media
    • Temporal data analysis, prediction, and forecasting; time series analysis
    • Communications and computer networks
    • Data mining and knowledge discovery
    • Power system applications
    • Financial engineering applications
    • Security applications
    • Applications in multi-agent systems and social computing
    • Manufacturing and industrial applications
    • Expert systems
    • Clinical applications
    • Big data applications
    • Other applications
    • Smart grid applications
    • Hybrid intelligent systems
    • Swarm intelligence
    • Sensor networks
    • Quantum computation
    • Computational biology
    • Molecular and DNA computation
    • Computation in tissues and cells
    • Artificial immune systems
    • Philosophical issues
    • Other cross-disciplinary topics


  • Mathematical and theoretical foundations
    • fuzzy measures and fuzzy integrals
    • fuzzy differential equations
    • fuzzy logic, fuzzy inference systems
    • aggregation, operators, fuzzy relations
  • Fuzzy control
    • optimal control of dynamic systems
    • adaptive and dynamically evolving process control
    • networked control systems
    • plantwide, monitoring, and supervisory control
  • Robotics and autonomous systems
    • navigation
    • decision making and situation awareness
    • handling systems
    • automated factories
    • smart industry
  • Fuzzy hardware, software, sensors, actuators, architectures
  • Fuzzy data and analysis
    • clustering, classification and pattern recognition
    • statistics and imprecise probabilities
    • data summarization
    • big data
    • time series modeling and forecasting
    • data analytics and visualization
    • social networks mining and analysis
  • Data management and web engineering
    • fuzzy data modeling
    • databases and information retrieval
    • data aggregation and fusion
    • fuzzy markup languages
  • Granular computing
    • type-2 fuzzy sets
    • intuitionistic fuzzy sets
    • higher order fuzzy sets
    • interval data processing
    • rough sets and relations
    • hybrid granular approaches
    • data clouds
  • Computational and artificial intelligence
    • fuzzy neural networks
    • fuzzy deep learning
    • fuzzy evolutionary algorithms
    • dynamically evolving fuzzy systems
    • fuzzy agent systems
    • knowledge representation and approximate reasoning
    • elicitation of fuzzy sets
    • explainable artificial intelligence
  • Otimization and operations research
    • fuzzy mathematical programming
    • possibilistic optimization
    • fuzzy algorithms and heuristic search
  • Decision analysis, multi-criteria decision making, and decision support
  • Fuzzy modeling, identification, and fault detection
  • Knowledge discovery
  • Fuzzy image, speech and signal processing, vision and multimedia data
  • Linguistic summarization, natural language processing
  • Applications
    • industry, technology, engineering
    • finance, business, economics
    • medicine, biological and social sciences
    • geographical information systems
    • social and communication networks
    • agriculture and environment engineering
    • security and mobility

Important Dates

  • 15 December 2017 – Tutorial, Special Sessions, Workshop and Competition Proposals
  • 15th January 2018 1st February 2018 – Paper Submission 
  • 15th March 2018 – Paper Acceptance
  • 1st May 2018 – Final Paper Submission
  • 1st May 2018 – Early Registration
  • 8-13 July 2018 – IEEE WCCI 2018, Rio de Janeiro, Brazil