Track Description
Knowledge Representation and Reasoning (KRR) is a well-established research area of Artificial Intelligence. 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. The scope of this track includes contributions to the formal foundations of KRR or that show the applicability of results to implemented or implementable systems. We also welcome papers from other areas that show the use of, or contributions to, the principles or practice of KRR. Consequently, KRR has contributed to the theory and practice of various areas in AI, such as automated planning and natural language understanding, among others, as well as to fields beyond AI, including databases, software engineering, the Semantic Web, computational biology, and the development of software agents.
Topics of Interest
- Action, change, causality and causal reasoning
- Argumentation
- Belief revision and update, belief merging, etc.
- Commonsense reasoning
- Constraint programming and KRR
- Contextual reasoning
- Description logics
- Diagnosis, abduction, explanation finding
- Inconsistency- and exception tolerant reasoning, paraconsistent logics
- KR and autonomous agents: intelligent agents, cognitive robotics, multi-agent systems
- KR and decision making, game theory, social choice
- KR and machine learning, inductive logic programming, knowledge discovery and acquisition
- KR and stream reasoning
- KR and the Web, Semantic Web
- Logic programming, answer set programming, constraint logic programming
- Non-monotonic logics, default logics, conditional logics
- Ontology formalisms and models
- Preferences: modeling and representation, preference-based reasoning
- Reasoners and solvers: SAT solvers, theorem provers, QBF solvers, and others
- Reasoning about knowledge and belief, dynamic epistemic logic, epistemic and doxastic logics
- Spatial reasoning and temporal reasoning, qualitative reasoning
- Uncertainty in KRR, representations of vagueness, many-valued and fuzzy logics
Track Chairs
- Matthias Knorr - Universidade NOVA de Lisboa
- Pedro Cabalar - Corunna University
- Ricardo Gonçalves - Universidade NOVA de Lisboa
