A comparative study of similarity measures
Fuzzy Sets and Systems
Introduction to AI Robotics
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Foundations of Databases: The Logical Level
Foundations of Databases: The Logical Level
Measures for Silhouettes Resemblance and Representative Silhouettes of Curved Objects
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Dynamics of Approximate Information Fusion
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Exploiting Rough Argumentation in an Online Dispute Resolution Mediator
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Towards Approximate BGI Systems
CEEMAS '07 Proceedings of the 5th international Central and Eastern European conference on Multi-Agent Systems and Applications V
Modeling and Reasoning with Paraconsistent Rough Sets
Fundamenta Informaticae
Tractable approximate knowledge fusion using the Horn fragment of serial propositional dynamic logic
International Journal of Approximate Reasoning
Four-valued extension of rough sets
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
A delegation-based architecture for collaborative robotics
AOSE'10 Proceedings of the 11th international conference on Agent-oriented software engineering
Agents in approximate environments
Games, Actions and Social Software
Modeling and Reasoning with Paraconsistent Rough Sets
Fundamenta Informaticae
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In real world applications robots and software agents often have to be equipped with higher level cognitive functions that enable them to reason, act and perceive in changing, incompletely known and unpredictable environments. One of the major tasks in such circumstances is to fuse information from various data sources. There are many levels of information fusion, ranging from the fusing of low level sensor signals to the fusing of high level, complex knowledge structures. In a dynamically changing environment even a single agent may have varying abilities to perceive its environment which are dependent on particular conditions. The situation becomes even more complex when different agents have different perceptual capabilities and need to communicate with each other. In this paper, we propose a framework that provides agents with the ability to fuse both low and high level approximate knowledge in the context of dynamically changing environments while taking account of heterogeneous and contextually limited perceptual capabilities. To model limitations on an agent's perceptual capabilities we introduce the idea of partial tolerance spaces. We assume that each agent has one or more approximate databases where approximate relations are represented using lower and upper approximations on sets. Approximate relations are generalizations of rough sets. It is shown how sensory and other limitations can be taken into account when constructing and querying approximate databases for each respective agent. Complex relations inherit the approximativeness of primitive relations used in their definitions. Agents then query these databases and receive answers through the filters of their perceptual limitations as represented by (partial) tolerance spaces and approximate queries. The techniques used are all tractable.