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Aims and scope

AI research must focus on theory, practice, and how to make machines more intelligent. AI Perspectives & Advances (AIPP) publishes high-quality work that aims to address key pieces of the “AI puzzle” –fundamental topics of the field that beg for a breakthrough to move AI forward– and practical applications thereof that are poised to inspire and define the next generation of AI-based automation. On the applied front we want to see representatives from all major industrial, societal, and governmental operations, but especially forward-looking operational prototypes tested in industry settings and lab mockups of practical deployment. In basic research we seek submissions on any and all topics related to cognition and relevant computational methods across the entire research focus spectrum, especially work that aims to bridge the gap between existing work in, and leap beyond, various subfields of AI. Defining clearly the questions that we seek to answer, and supporting the selection with arguments from more than one perspective, should be fundamental in every research field. The same must hold for the methods chosen to answer them. Any methodology that is fruitful should be considered good. We welcome any papers proposing or using unified theories of cognition and AI research methodology, separately or in relation to other topics.

Topics of interest include, but are not limited to:

Learning & Knowledge Representation

Experimental, constructivist & cumulative learning
- how actionable knowledge can be built incrementally from ongoing information gathering
- how an intelligent agent can use systematic experimentation to create new knowledge

Analogy-based learning
- how analogies at various level of detail can allow an intelligent agent to learn faster by creating useful hypotheses about novel phenomena

Representation of cause-effect relations
- how an intelligent agent can model the world based on experience
- the relation between cause-effect relations and grounded knowledge

Concept-based knowledge representation
- what role do concepts play in cognition and how they are represented
- language use and language development

-  what kinds of models enable understanding and how does it relate to reasoning
- how a machine can automatically improve its understanding of its environment
- what role do concepts play in cognition and how they are represented
- what is the relation between understanding and ‘common sense’

Reasoning & Generality

Reasoning for generality
- methods for generalizing (inducing) from limited examples, using existing knowledge to support cumulative learning

Effective general control
- giving systems a way to handle vast amounts of information and operate under insufficient knowledge and resources

Automatic explanation generation
- how a system can generate useful explanations of its actions, knowledge, and environment

Ampliative reasoning
- how an intelligent system can combine many types of reasoning to learn tasks, solve problems, and make plans

Ampliative control
- how an intelligent system can combine many types of control - reactive, predictive, reflective - in the process of learning, solving problems, and making plans

Generality & Cognitive Autonomy

Worlds with infinite complexity & Empirical (“real-world”) control
- methods for achieving generality and autonomy in worlds with infinite combinatorics and novel phenomena, like the physical world, as opposed to axiomatic tasks like board games
- approaches and perspectives on unification of multiple control strategies for achieving goals and making plans

Autonomous knowledge management
- how an intelligent system can incrementally and autonomously build actionable and expandable models from its ongoing experience in a self-supervised manner

Seed-programmed autonomy & learning
- what is sufficient for an intelligent agent to know at “birth” to allow it to bootstrap its learning (from a “seed”), without outside help

General-purpose planning
- enabling systems to learn plan-making in a domain-independent way

Autonomous goal generation & management
- methods for achieving autonomous generation and management of subgoals
- approaches to autonomously managing multiple goals, goal evaluation, conflicting goals, goal prioritization, goal modification and goal abandonment

- how an intelligent control system can inspect its own operation, steer its development, and improve it over time through cumulative learning

Many of these topics are increasingly mentioned by leading AI researchers as being of central importance to the advancement of AI. Attempts to address them in a holistic way have largely been based on methodologies dating back to the middle of last century, before the advent of modern programming techniques. Among them are formal logic and artificial neural networks, both of which have well-known limitations for achieving general autonomy, reasoning, and learning. Examples include methods for. To create truly intelligent machines requires integration of these (and other) cognitive processes in a single unified system that is capable of cumulative learning, autonomous learning of cause-effect relations, automatic explanation generation, and empirical (“real-world”) reasoning. Such unification calls for new approaches consisting of a coherently matching theory, methodology, and cognitive architecture – the “wholly trinity of AI research”. The Journal of AI Perspectives & Advances is not limited to these topics, but welcomes any and all research addressing these, as well as other, topics of AI research.

Open access

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Peer-review policy

Peer-review is the system used to assess the quality of a manuscript before it is published. Independent researchers in the relevant research area assess submitted manuscripts for originality, validity and significance to help editors determine whether the manuscript should be published in their journal. You can read more about the peer-review process here.

AI Perspectives & Advances operates a single-blind peer-review system, where the reviewers are aware of the names and affiliations of the authors, but the reviewer reports provided to authors are anonymous. Publication of research articles by AI Perspectives & Advances is dependent primarily on their scientific validity and coherence as judged by our external expert editors and/or peer reviewers, who will also assess whether the writing is comprehensible and whether the work represents a useful contribution to the field. Submitted manuscripts will generally be reviewed by two to three experts who will be asked to evaluate whether the manuscript is scientifically sound and coherent, whether it duplicates already published work, and whether or not the manuscript is sufficiently clear for publication. Reviewers will also be asked to indicate how interesting and significant the research is. The Editors will reach a decision based on these reports and, where necessary, they will consult with members of the Editorial Board.

Citing articles in AI Perspectives & Advances

Articles in AI Perspectives & Advances should be cited in the same way as articles in a traditional journal. Because articles are not printed, they do not have page numbers; instead, they are given a unique article number.

Article citations follow this format:

Authors: Title. AI Perspect. Adv. [year], [volume number]:[article number].

e.g. Roberts LD, Hassall DG, Winegar DA, Haselden JN, Nicholls AW, Griffin JL: Increased hepatic oxidative metabolism distinguishes the action of Peroxisome Proliferator-Activated Receptor delta from Peroxisome Proliferator-Activated Receptor gamma in the Ob/Ob mouse. AI Perspect. Adv. 2009, 1:115.

1:115 refers to article 115 from Volume 1 of the journal.

Editorial policies

All manuscripts submitted to AI Perspectives & Advances should adhere to SpringerOpen's editorial policies.

Once your article is accepted, it will be processed by production and published shortly afterwards. In some cases, articles may be held for a short period of time prior to publication. If you have any concerns or particular requirements please contact the Journal.

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Authors who wish to appeal a rejection or make a complaint should follow the procedure outlined in the BMC Editorial Policies.

Benefits of publishing with SpringerOpen

High visibility

AI Perspectives & Advances's open access policy allows maximum visibility of articles published in the journal as they are available to a wide, global audience. 

Speed of publication

AI Perspectives & Advances offers a fast publication schedule whilst maintaining rigorous peer review; all articles must be submitted online, and peer review is managed fully electronically (articles are distributed in PDF form, which is automatically generated from the submitted files). Articles will be published with their final citation after acceptance, in both fully browsable web form, and as a formatted PDF; the article will then be available through AI Perspectives & Advances and SpringerOpen.


Online publication in AI Perspectives & Advances gives you the opportunity to publish large datasets, large numbers of color illustrations and moving pictures, to display data in a form that can be read directly by other software packages so as to allow readers to manipulate the data for themselves, and to create all relevant links (for example, to PubMed, to sequence and other databases, and to other articles).

Promotion and press coverage

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Annual Journal Metrics - 2021

  • 2023 Speed
    41 days submission to first editorial decision for all manuscripts (Median)
    141 days submission to accept (Median)

    2023 Usage 
    4 Altmetric mentions 

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