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Artificial Intelligence Review
Kluwer Academic Publishers
  1-10 of 40 reviews Date Reviewed 
  Evolution or revolution: the critical need in genetic algorithm based testing
Surendran A., Samuel P.  Artificial Intelligence Review 48(3): 349-395, 2017. Type: Article

Software testing consumes a considerable amount of the cost and time of software development, but it is one of the most crucial phases; it cannot be skipped. Software testing is therefore referred to as “a necessary evil.” In the past ...

Dec 1 2017
  A survey of imperatives and action representation formalisms
Srinivasan B., Parthasarathi R.  Artificial Intelligence Review 48(2): 263-297, 2017. Type: Article

What is an action? The agent observes two states at different times; if there is a change, then an action occurred. This implicit definition of action is adopted in this paper. At the beginning of artificial intelligence (AI), first-order logic wa...

Nov 20 2017
  A multiagent, dynamic rank-driven multi-deme architecture for real-valued multiobjective optimization
Acan A., Lotfi N.  Artificial Intelligence Review 48(1): 1-29, 2017. Type: Article

Solutions for multiobjective optimization problems find use in architectures that support parallel processing. A new method proposed by Acan and Lotfi is seen to dominate over most other optimization solutions, as is the case with a typical Pareto...

Nov 1 2017
  A comparative empirical study on social media sentiment analysis over various genres and languages
Hangya V., Farkas R.  Artificial Intelligence Review 47(4): 485-505, 2017. Type: Article

This paper compares two types of sentiment analysis (SA), global sentiments and the opinions of people regarding a specific target topic. Document-level SA can decide the polarity of a document at a global level, while target-level SA determines t...

May 1 2017
  Brain storm optimization algorithm: a review
Cheng S., Qin Q., Chen J., Shi Y.  Artificial Intelligence Review 46(4): 445-458, 2016. Type: Article

This paper is a review of research in the area of brain storm optimization (BSO) algorithms that examines the “more effective algorithms and search strategies.” This type of technique has application in the real world, for example, to ...

Apr 6 2017
  Aspect extraction in sentiment analysis: comparative analysis and survey
Rana T., Cheah Y.  Artificial Intelligence Review 46(4): 459-483, 2016. Type: Article

This paper covers a considerable volume of research work in the area of sentiment analysis. The authors define sentiment analysis as the computational classification of the context of a user’s feelings within a passage of text, primarily tex...

Mar 23 2017
  A dimensionality reduction method based on structured sparse representation for face recognition
Gu G., Hou Z., Chen C., Zhao Y.  Artificial Intelligence Review 46(4): 431-443, 2016. Type: Article

Face recognition (FR) is considered to be a typical machine learning problem. Among all FR algorithms, popular models include classical linear models such as eigen face, nonlinear models such as manifold learning, and sparse representation-based c...

Jan 23 2017
  Application of intelligent agents in health-care: review
Iqbal S., Altaf W., Aslam M., Mahmood W., Khan M.  Artificial Intelligence Review 46(1): 83-112, 2016. Type: Article

Along with the successful use of intelligent agents in the healthcare domain, researchers have been motivated to study the application of software engineering knowledge in this field....

Nov 3 2016
  Novelty detection in data streams
Faria E., Gonçalves I., de Carvalho A., Gama J.  Artificial Intelligence Review 45(2): 235-269, 2016. Type: Article

Novelty detection, an important topic in machine learning, is the ability of a classification system to differentiate between known and unknown objects in a pattern. Novelty detection has many real-world applications, like in signal processing, pa...

Oct 12 2016
  Exponential moving average based multiagent reinforcement learning algorithms
Awheda M., Schwartz H.  Artificial Intelligence Review 45(3): 299-332, 2016. Type: Article

Reinforcement learning for multiagent systems aims to find optimal policies that can be learned by agents during their interaction in cooperative or competitive games. In game theory, the target is reaching the Nash equilibrium, where each agent i...

Jun 15 2016
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