By Hector Geffner (auth.), Ron van der Meyden, Jan-Georg Smaus (eds.)
ISBN-10: 3642206735
ISBN-13: 9783642206733
This ebook offers revised types of chosen papers from the sixth Workshop on version Checking and synthetic Intelligence, MoChArt 2010, held in Atlanta, GA, united states in July 2010, in addition to papers contributed next to the workshop.
The 7 papers awarded have been conscientiously reviewed and chosen for inclusion during this booklet. furthermore, the ebook additionally includes a longer summary of the invited speak held on the workshop.
The issues lined by way of those papers are normal seek algorithms, software of AI ideas to computerized software verification, multiagent structures and epistemic good judgment, abstraction, epistemic version checking, and thought of version checking.
Read or Download Model Checking and Artificial Intelligence: 6th International Workshop, MoChArt 2010, Atlanta, GA, USA, July 11, 2010, Revised Selected and Invited Papers PDF
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Extra info for Model Checking and Artificial Intelligence: 6th International Workshop, MoChArt 2010, Atlanta, GA, USA, July 11, 2010, Revised Selected and Invited Papers
Sample text
Condition> has to be satisfied each time the program reaches VASSERT. – RANGE(
Parse tree and dependency graph of a sample C++ program in our tool Program Model Checking via Action Planning 39 The state of a program includes information like assignments to global and local variables, as well as stack and dynamic memory contents. We distinguish binary, integeror real-valued variables. The situation before the execution of a program is called initial state, and the state of a program at its termination is called (valid) end state. Additionally to the variable assignments, a state contains information about the program counter, denoting which transition has been or will be executed.
The inverse, however, is not true. Considering the fundamental difference in the architectures, specialized solutions developed for multi-core model checking may not easily transfer to the GPU. The actual code is written for CUDA supporting NVIDIA hardware. For other vendors the implementation of the pseudo-code algorithms have to be adapted. One also may combine the three stages into two avoiding some transfer between RAM and VRAM. Our core design objective, however, was to maximize memory usage to increase parallelism on the card.
Model Checking and Artificial Intelligence: 6th International Workshop, MoChArt 2010, Atlanta, GA, USA, July 11, 2010, Revised Selected and Invited Papers by Hector Geffner (auth.), Ron van der Meyden, Jan-Georg Smaus (eds.)
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