Journal Information
Artificial Intelligence (AI)
http://www.journals.elsevier.com/artificial-intelligence/
Impact Factor:
3.034
Publisher:
ELSEVIER
ISSN:
0004-3702
Viewed:
7863
Tracked:
36

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Call For Papers
Artificial Intelligence, which commenced publication in 1970, is now the generally accepted premier international forum for the publication of results of current research in this field. The journal welcomes foundational and applied papers describing mature work involving computational accounts of aspects of intelligence. Specifically, it welcomes papers on:

    Artificial Intelligence and Philosophy
    Automated reasoning and inference
    Case-based reasoning
    Cognitive aspects of AI
    Commonsense reasoning
    Constraint processing
    Heuristic search
    High-level computer vision
    Intelligent interfaces
    Intelligent robotics
    Knowledge representation
    Machine learning
    Multiagent systems
    Natural language processing
    Planning and theories of action
    Reasoning under uncertainty or imprecision 

The journal reports results achieved; proposals for new ways of looking at AI problems must include demonstrations of effectiveness. Papers describing systems or architectures integrating multiple technologies are welcomed. Artificial Intelligence (AIJ) also invites papers on applications, which should describe a principled solution, emphasize its novelty, and present an in-depth evaluation of the AI techniques being exploited. The journal publishes an annual issue devoted to survey articles and also hosts a "competition section" devoted to reporting results from AI competitions. From time to time, there are special issues devoted to a particular topic; such special issues always have open calls.

Artificial Intelligence caters to a broad readership. Papers that are heavily mathematical in content are welcome but should be preceded by a less technical introductory section that is accessible to a wide audience. Papers that are only mathematics, without demonstrated applicability to Artificial Intelligence problems may be returned.
Last updated by Dou Sun in 2018-09-11
Special Issues
Special Issue on Explainable Artificial Intelligence
Submission Date: 2020-03-01

As intelligent systems become more widely applied (robots, automobiles, medical & legal decision-making), users and the general public are becoming increasingly concerned with issues of understandability and trust. The current generation of Intelligent systems based on machine learning seem to be inscrutable. Consequently, explainability has become an important research topic, in both research literature and the popular press. These considerations in the public discourse are partly responsible for the establishment of projects like DARPA's Explainable AI Project, European response to the General Data Protection Regulation, and the recent series of XAI Workshops at major AI conferences such as IJCAI. In addition, because "Explainability" is inherently about helping humans understand intelligent systems, XAI is also gaining interest in the human computer interaction (HCI) community. The creation of explainable intelligent systems requires at least two major components. First, explainability is an issue of human-AI interaction; and second, it requires the construction of representations that support the articulation of explanations. The achievement of Explainable AI requires interdisciplinary research that encompasses Artificial Intelligence, social science, and human-computer interaction. A recent survey published in AIJ (https://doi.org/10.1016/j.artint.2018.07.007) shows that there is a rich understanding in philosophy and cognitive & social psychology of how humans explain concepts to themselves and to others. This work addresses the development of a framework for the first issue noted above: what counts as an explanation to support the HCI aspects of XAI. The second challenge of building models to support explanation (especially in intelligent systems based on aspects of machine learning) is more scattered, ranging from from recursive application of deep learning all the way to the induction of logical causal models. This special issue seeks contributions on foundational studies in Explainable Artificial Intelligence. In particular, we seek research articles that address the fact that explainable AI is both a technical problem and a human problem, and scientific work on explainable AI must consider that it is ultimately humans that need to understand technology. The importance of the topic of Explainable AI is manifested by the number of conferences and conference sessions that on the topic that have been announced in recent months, along with calls for reports on explainability in specialized areas, such as robotics, planning, machine learning, optimisation, and multi-agent systems. Topics Human-centric Explainable AI:Submissions with the flavor of both an AI research report and a report on a human-behavioural experiment are of particular interest. Such submissions must convey details of the research methods (experimental designs, control conditions, etc.). There should be a presentation of results that adduce convincing empirical evidence that the XAI processes achieve genuine success at explaining to its intended users. Theoretical and Philosophical Foundations:We invite submissions on the philosophical, theoretical or methodological issues in Explainable AI (XAI). In particular, we encourage submissions that go beyond standard issues of interpretability and casual attribution, and into foundations of how to provide meaningful insights from AI models that are useful for people other than computer scientists. Explainable Black-box Models:We invite submissions that investigate how to provide meaningful and actionable insights on black-box models, especially machine learning approaches using opaque models such as deep neural networks. In particular, we encourage submissions that go beyond the extraction of interpretable features; for example, considering explanation as a process, building user mental models, contrastive explanation, etc. Knowledge Representation and Machine Learning:Submissions that investigate the use of knowledge representation techniques, including user modelling, abductive reasoning, diagnosis, etc., are of interest. In particular, we encourage submissions that capitalize on the strengths of knowledge representation and explainable machine learning. Interactive Explanation:Submissions are of interest if they report on research in which human users or learners interact with intelligent systems in an explanation modality, which leads to improvement in the performance of the human-machine work system. Submissions that regard explanation as an exploratory, interactive process are of particular interest. This is in contrast with the model that considers explanation as a one-way paradigm. Historical Perspectives:One additional topic of particular interest is Expert Systems, since many of the current issues of interpretability, explainability, and explanation as a process first emerged in the era of Expert Systems. Brief historical retrospections on the fundamental problems are encouraged. Case Study Reports:We invite short reports outlining case studies illustrating the consequences of a lack of explainability in intelligence systems, with the aim of providing motivating examples/benchmarks and challenge problems for the community.
Last updated by Dou Sun in 2019-06-09
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