By Kryštof Hoder, Laura Kovács, Andrei Voronkov (auth.), Ildar Batyrshin, Grigori Sidorov (eds.)
The two-volume set LNAI 7094 and LNAI 7095 constitutes the refereed complaints of the tenth Mexican foreign convention on synthetic Intelligence, MICAI 2011, held in Puebla, Mexico, in November/December 2011. The ninety six revised papers offered have been rigorously reviewed and chosen from quite a few submissions. the 1st quantity comprises 50 papers representing the present major themes of curiosity for the AI group and their functions. The papers are equipped within the following topical sections: automatic reasoning and multi-agent structures; challenge fixing and computer studying; typical language processing; robotics, making plans and scheduling; and scientific purposes of man-made intelligence.
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Additional info for Advances in Artificial Intelligence: 10th Mexican International Conference on Artificial Intelligence, MICAI 2011, Puebla, Mexico, November 26 - December 4, 2011, Proceedings, Part I
The main problem is the large number of generated properties. On the one hand, one can say that this is an accolade to the method that it can generate many more properties than a human would ever be able to discover. On the other hand, one can say that the majority of generated properties are uninteresting. This poses the following problems that can help us understand computer reasoning and intelligence better: 1. what makes some program properties more interesting than others from the viewpoint of programmers (or applications); 2.
A) First, we can add the negation of the property to the formulas obtained by program analysis and try to prove that the resulting set of formulas is unsatisfiable. It is easier than invariant generation since one does not have to take care of colors and can use arbitrary proofs, ordering and strategies, including goal-oriented ones. (b) Second, one can prove that the property is an inductive invariant, which is a much simpler problem and can be reduced to proving a few formulas with respect to the theory.
Inputs to the parser are (large) C programs. After parsing, Vampire finds all loops in the program and checks, for each loop, if it is as given in (1) and thus can be analysed. Vampire outputs a set of loop invariants for each loop (1) under analysis. Figure 1 illustrates the invariant generation process within Vampire. 1 Challenging Benchmarks Table 3 describes the effect of symbol elimination on 6 programs. The names of the first 5 programs and their origins (that is, the papers where they were described) are given in column 1.
Advances in Artificial Intelligence: 10th Mexican International Conference on Artificial Intelligence, MICAI 2011, Puebla, Mexico, November 26 - December 4, 2011, Proceedings, Part I by Kryštof Hoder, Laura Kovács, Andrei Voronkov (auth.), Ildar Batyrshin, Grigori Sidorov (eds.)