Knowledge Representation and Reasoning (KRR) is an exciting, well-established field of research. In KRR a fundamental assumption is that an agent\’s knowledge is explicitly represented in a declarative form, suitable for processing by dedicated reasoning engines. This assumption, that much of what an agent deals with is knowledge-based, is common in many modern intelligent systems. Consequently, KRR has contributed to the theory and practice of various areas in AI, such as automated planning, natural language understanding, among others, as well as to fields beyond AI, including databases, verification, and software engineering. In recent years KRR has contributed to new and emerging fields including the semantic web, computational biology, and the development of software agents.
The KR conference series is the leading forum for timely in-depth presentation of progress in the theory and principles underlying the representation and computational management of knowledge.
We solicit two kinds of papers: full papers, presenting novel results on the principles of KRR that clearly contribute to the formal foundations of relevant problems or show the applicability of results to implemented or implementable systems; and short papers describing applications, systems and/or demos, reports from the field of applications, experiments, developments, and tests. We also welcome (full or short) papers from other areas that show clear use of, or contributions to, the principles or practice of KRR.
The best paper of the conference will receive the 2018 Ray Reiter Best Paper Prize, and the best student paper, whose main author is a student, will receive the 2018 Marco Cadoli Student Paper Prize. In addition, a few selected papers from KR 2018 will have the opportunity for fast-track publication in the AI Journal, and the best 1-2 papers in the area of logic programming or answer set programming will have the opportunity for fast-track publication in TPLP.
Topics of interest include, but are not limited to:
Belief revision and update, belief merging, etc.
Computational aspects of knowledge representation
Concept formation, similarity-based reasoning
Explanation finding, diagnosis, causal reasoning, abduction
Inconsistency- and exception tolerant reasoning, paraconsistent logics
KR and autonomous agents: intelligent agents, cognitive robotics, multi-agent systems
KR and game theory
KR and machine learning, inductive logic programming, knowledge discovery and acquisition
KR and natural language processing
KR and the Web, Semantic Web
Logic programming, answer set programming, constraint logic programming
Multi- and order-sorted representations and reasoning
Nonmonotonic logics, default logics, conditional logics
Philosophical foundations of KR
Ontology formalisms and models
Preference modeling and representation, reasoning about preferences
Qualitative reasoning, reasoning about physical systems
Reasoning about actions and change, action languages, situation calculus, dynamic logic
Reasoning about knowledge and belief, epistemic and doxastic logics
Spatial reasoning and temporal reasoning
Uncertainty, representations of vagueness, many-valued and fuzzy logics