AIICS

Jonas Kvarnström

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2014
[2] Full text  Patrick Doherty, Jonas Kvarnström, Mariusz Wzorek, Piotr Rudol, Fredrik Heintz and Gianpaolo Conte. 2014.
HDRC3 - A Distributed Hybrid Deliberative/Reactive Architecture for Unmanned Aircraft Systems.
In Kimon P. Valavanis, George J. Vachtsevanos, editors, Handbook of Unmanned Aerial Vehicles, pages 849–952. Springer Science+Business Media B.V.. ISBN: 978-90-481-9706-4, 978-90-481-9707-1.
DOI: 10.1007/978-90-481-9707-1_118.
Find book at a Swedish library/Hitta boken i ett svenskt bibliotek: http://libris.kb.se/bib/16541662
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This chapter presents a distributed architecture for unmanned aircraft systems that provides full integration of both low autonomy and high autonomy. The architecture has been instantiated and used in a rotorbased aerial vehicle, but is not limited to use in particular aircraft systems. Various generic functionalities essential to the integration of both low autonomy and high autonomy in a single system are isolated and described. The architecture has also been extended for use with multi-platform systems. The chapter covers the full spectrum of functionalities required for operation in missions requiring high autonomy. A control kernel is presented with diverse flight modes integrated with a navigation subsystem. Specific interfaces and languages are introduced which provide seamless transition between deliberative and reactive capability and reactive and control capability. Hierarchical Concurrent State Machines are introduced as a real-time mechanism for specifying and executing low-level reactive control. Task Specification Trees are introduced as both a declarative and procedural mechanism for specification of high-level tasks. Task planners and motion planners are described which are tightly integrated into the architecture. Generic middleware capability for specifying data and knowledge flow within the architecture based on a stream abstraction is also described. The use of temporal logic is prevalent and is used both as a specification language and as an integral part of an execution monitoring mechanism. Emphasis is placed on the robust integration and interaction between these diverse functionalities using a principled architectural framework. The architecture has been empirically tested in several complex missions, some of which are described in the chapter.

2009
[1] Full text  Patrick Doherty and Jonas Kvarnström. 2009.
Temporal Action Logics.
In V. Lifschitz, F. van Harmelen, and F. Porter, editors, Handbook of Knowledge Representation, pages 709–757. In series: Foundations of Artificial Intelligence #3. Elsevier. ISBN: 978-0-444-52211-5.
DOI: 10.1016/S1574-6526(07)03018-0.
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The study of frameworks and formalisms for reasoning about action and change [67, 58, 61, 65, 70, 3, 57] has been central to the knowledge representation field almost from the inception of Artificial Intelligence as a general field of research [52, 56]. The phrase “Temporal Action Logics” represents a class of logics for reasoning about action and change that evolved from Sandewall’s book on Features and Fluents [61] and owes much to this ambitious project. There are essentially three major parts to Sandewall’s work. He first developed a narrative-based logical framework for specifying agent behavior in terms of action scenarios. The logical framework is state-based and uses explicit time structures. He then developed a formal framework for assessing the correctness (soundness and completeness) of logics for reasoning about action and change relative to a set of well-defined intended conclusions, where reasoning problems were classified according to their ontological or epistemological characteristics. Finally, he proposed a number of logics defined semantically in terms of definitions of preferential entailment1 and assessed their correctness using his assessment framework.