2nd Workshop on Cognitive Computing and Applications for Augmented Human Intelligence, in conjunction with International Joint Conference on Artificial Intelligence (IJCAI), at Buenos Aires, Argentina, July 25-31, 2015

Sister Workshop

1st Workshop on Cognitive Knowledge Acquisition (Cognitum 2015) also at IJCAI 2015.

Previous Workshop

1st CCAHI Workshop was at AAAI 2014, Quebec City, Canada.


Date of workshop is not yet confirmed (25-27 Jul 2015).

Workshop paper submissions due
May 15, 2015
Notification to authors May 25, 2015

Camera-ready copies of authors’ papers
May 30, 2015   


Papers should be submitted on Easychair here.

Paper Guidelines

All papers submissions must be in AAAI format. They can be of two types. The first is regular research papers, which can be up to 6 pages long + 1 for reference, and are expected to present a significant contribution. The second is short submission of up to 4 pages + 1 for reference, which describes a position on the topic of the workshop or a demonstration/tool.


[20 May 2015] CCAAHI workshop is being merged with Cognitum 2015 workshop, the event on Cognitive Knowledge Acquisition and Applications. Please see the site for latest and expanded information.
[18 May 2015] Prof. Milind Tambe of USC will be an invited speaker on "Green Security Games".
[15 Feb 2015] Submission site is up.


“Cognitive Computing” refers to automated agents that can learn complex tasks, interact with humans via natural interfaces and take autonomous decisions and actions working with individual and group of humans. It represents a new generation of computing systems enabling genuine human-machine collaboration where the system is able to understand high-level objectives specified by humans in a natural language, autonomously learn how to achieve the objectives from data in the domain, report results back to humans, and iterate the interactions via sequential dialog until the objectives are achieved.  As building and deploying such systems may require major platform improvements with respect to size, power usage, etc., there is also a significant focus in Cognitive Computing on alternative hardware, such as brain-inspired or other non-von Neumann architectures.

The thesis of this workshop is to unite and seek synergies across highlighted areas of AI research where exciting research progress has been made in recent years, and lead them to smarter decisions and actions for benefit of society. The proposers organized the 1st such workshop at AAAI 2014, Quebec City, Canada. The 2nd workshop will serve as a forum to continue the discussion and specifically highlight the promising new applications that are emerging. Among AI areas, the first area is new learning techniques that may have the potential to automatically learn complex tasks by directly training on massive amounts of raw data, much of which may be unlabeled, unstructured, and multi-modal in form (natural language text/speech, audio, video, etc.).  These techniques include deep learning, manifold learning, sparsity-based techniques, and transfer/cross-modal learning and inference methods.  Researchers employing such techniques have recently achieved quantum performance leaps in speech and image recognition tasks, and have also demonstrated the ability to learn complex feature representations entirely from unlabeled data.  The second area has to do with enabling computers to understand and work with naturalistic input from humans, in the form of natural language speech or text, visual input such as gestures or facial expressions, and haptic (touch-based) inputs.  The most exciting demonstrations of these capabilities in the last few years include Question-Answering systems such as Watson and Wolfram Alpha, and commercially deployed personal assistant technology such as Siri, Google Now, Dragon Mobile Assistant, Nina, and TellMe. With the improved insights from the these trends, the systems would like to lead humans to take better decisions and actions individually as well as groups. Such human computation situations are plentiful and can happen when humans face information overload (e.g., driving, customer service delivery), cognition impairment (e.g., Alzheimer) or take collective, multi-objective, decisions (e.g., conference program scheduling, disaster response). 

In traditional AI, humans are not part of the equation, yet in cognitive computing, humans and machines work together. To enable a natural interaction between them, cognitive computing systems use image and speech recognition as their eyes and ears to understand the world and interact more seamlessly with humans. It provides a feedback loop for machines and humans to learn from and teach one another. By using visual analytics and data visualization techniques, cognitive computers can display data in a visually compelling way that enlightens humans and helps them make decisions based on data. Cognitive computing systems get better over time as they build knowledge and learn a domain - its language and terminology, its processes and its preferred methods of interacting. Unlike expert systems of the past, which required rules to be hard coded into a system by a human expert, cognitive computers can process natural language and unstructured data and learn by experience, much in the same way humans do. While they will have deep domain expertise, instead of replacing human experts, cognitive computers will act as a decision support system and help them make better decisions based on the best available data, whether in healthcare, finance or customer service.

The workshop will include invited talks and panel discussions with key researchers in this emerging area of cognitive computing. We will also select peer-reviewed technical papers, demonstrations and posters to be presented in dedicated sessions. The program committee will review the submissions and they will appear in a digital proceeding to be finalized as per IJCAI’s guidance.

Topics of interest include, but not restricted to, are: 

  1. What does cognitive computing mean to AI researchers?
  2. What does cognitive computing mean to Neuroscience researchers?
  3. What does cognitive computing mean to Hardware researchers?
  4. What are the differences of Cognitive Computing from AI and what are the new sets of challenges?
  5. What are the test beds of cognitive computing?
  6. What are the early applications of cognitive computing systems?
  7. What are the early architectures that allow for the closed cognitive loop, from sensors to actions?
  8. What are the emerging machine learning technologies that address the big data challenges implied by cognitive computing applications?
  9. What are the early augmented cognition technologies?
  10. How can cognitive computing techniques improve human computation, and what demands do the latter put on the former?
  11.  Ethical and legal aspects of machine-suggested actions


Biplav Srivastava, IBM Research - India, New DelhiEmail: sbiplav AT in.ibm.com  (Designated Contact)
Janusz Marecki, IBM TJ Watson Research Center, USA; Email: marecki AT us.ibm.com
Gerald Tesauro, IBM TJ Watson Research Center, USA; Email: gtesauro AT us.ibm.com

Program Committee

Rosario U-Sosa, IBM TJ Watson Research Center, USA
N S Narayanswamy, IIT Madras, India
Pat Langley, CMU, USA and University of Auckland, New Zealand
(* more confirmations pending *)

Schedule (planned)

Workshop Notes