Special Issue "Active Learning and Reasoning in Autonomous Intelligent Agents"

A special issue of Future Internet (ISSN 1999-5903).

Deadline for manuscript submissions: 20 April 2020.

Special Issue Editors

Dr. Corrado Santoro
E-Mail Website
Guest Editor
Department of Mathematics and Informatics, University of Catania, Viale Andrea Doria, 6, 95125 Catania CT, Italy
Interests: autonomous intelligent agents; complex and emerging systems; robotics; embedded computing programming; grid systems
Prof. Dr. Stefania Monica
E-Mail Website
Guest Editor
Department of Mathematics, Physics and Computer Science, University of Parma, 43124 Parma, Italy
Interests: indoor localization; constraint satisfaction problems; emerging behaviours in large multi-agent systems

Special Issue Information

Dear Colleagues,

For 20 years, autonomous agents have represented a different way of thinking about software architectures for complex systems that have a strong connection with a reference environment. Their objective is to make situated systems behave by making autonomous decisions in order to reach—in an optimal or suboptimal way—a certain goal. Common techniques also included the adoption of several cooperating agents to form a so-called multi-agent system, with the aim of subdividing a big and complex goal into multiple (and less complex) sub-goals that are easier to achieve. In this sense, the aspects related to interaction and cooperation became particularly important, as well as the techniques and approaches used to program the autonomous behaviour of agents.

A fundamental aspect in agent programming is the way in which data coming from the environment is analysed and exploited in order to extract meaningful information, which is useful to plan proper actions leading to goal achievement. In the context of intelligent systems, classical techniques include knowledge-based approaches that use logic representation of data and reasoning; however, in the recent years, learning techniques, introduced in the '80s with the birth of neural networks, have experienced a renaissance thanks to the availability of new neural models and high-performance computing platforms.

On this basis, this Special Issue has the objectives of gathering recent advances in the research of algorithms, approaches, techniques, and tools for the online extraction of meaningful data and letting agents “consciously” behave to reach a certain objective. Papers focusing on both learning approaches and logic-based reasoning are welcome; however, other proposals that include the cited techniques in their work-flow are also strongly encouraged.

We welcome topics including but are not limited to the following:

  • On-line learning techniques and algorithms;
  • On-line agent-based data analysis approaches;
  • Knowledge representation and manipulation;
  • Languages and paradigms for reasoning and behaviour programming;
  • Applications of intelligent agents in the context of learning-based or knowledge-based systems.

Dr. Corrado Santoro
Prof. Dr. Stefania Monica
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • Intelligent agents
  • Multi-agent systems
  • Online learning
  • Reasoning


Published Papers (1 paper)

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Open AccessArticle
High-Level Smart Decision Making of a Robot Based on Ontology in a Search and Rescue Scenario
Future Internet 2019, 11(11), 230; https://doi.org/10.3390/fi11110230 - 31 Oct 2019
The search and rescue (SAR) scenario is complex and uncertain where a robot needs to understand the scenario to make smart decisions. Aiming at the knowledge representation (KR) in the field of SAR, this paper builds an ontology model that enables a robot [...] Read more.
The search and rescue (SAR) scenario is complex and uncertain where a robot needs to understand the scenario to make smart decisions. Aiming at the knowledge representation (KR) in the field of SAR, this paper builds an ontology model that enables a robot to understand how to make smart decisions. The ontology is divided into three parts, namely entity ontology, environment ontology, and task ontology. Web Ontology Language (OWL) is adopted to represent these three types of ontology. Through ontology and Semantic Web Rule Language (SWRL) rules, the robot infers the tasks to be performed according to the environment state and at the same time obtains the semantic information of the victims. Then, the paper proposes an ontology-based algorithm for task planning to get a sequence of atomic actions so as to complete the high-level inferred task. In addition, an indoor experiment was designed and built for the SAR scenario using a real robot platform—TurtleBot3. The correctness and usability of the ontology and the proposed methods are verified by experiments. Full article
(This article belongs to the Special Issue Active Learning and Reasoning in Autonomous Intelligent Agents)
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