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Real-world knowledge-based systems must deal with information coming from different sources, leading to uncertainty due to incompleteness, inconsistency, and/or inherent uncertainty (such as the uncertainty present in very complex systems such as the stock market or the weather). Instead of considering such uncertain information useless, knowledge engineers face the challenge of putting it to good use when solving a wide range of problems. Argumentation is a useful approach in this setting – reasons for and against a claim are analyzed to decide on an outcome, much in the same way as organized human discussions are carried out. An important byproduct of such analyses is an accompanying explanation that can be leveraged to decide if there is information that should be used differently, discarded, or there is further information to be contemplated.
Since there has recently been increasing demand for explainability of intelligent systems that operate in certain domains that have high-impact consequences, such as medical or legal decisions, argumentation-based approaches are ideal for query answering and reasoning systems that contemplate human-in-the-loop models to tackle the challenges of incompleteness and inconsistency in data. This special issue should be of interest to the AI community focusing on practical applications in:
- Explainability and interpretability of results
- Hybrid KR&R-machine learning approaches
- Query answering under different kinds of uncertainty
- Cybersecurity
- Semantic web
- Stream reasoning
- Multi-agent systems
All submissions must comply with the submission guidelines of IEEE Intelligent Systems (https://www.computer.org/
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Contact the guest editors at is2-21@computer.org.
