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This booklet constitutes the refereed court cases of the thirty eighth Annual German convention on man made Intelligence, KI 2015, held in Dresden, Germany, in September 2015.

The 15 revised complete technical papers awarded including 14 technical communications, four doctoral consortium contributions, and three keynotes have been rigorously reviewed and chosen from fifty eight submissions.

The convention offers the chance to provide a much broader diversity of effects and concepts which are of curiosity to the KI viewers, together with experiences approximately contemporary personal guides, place papers, and previews of ongoing work.

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Extra resources for KI 2015: Advances in Artificial Intelligence: 38th Annual German Conference on AI, Dresden, Germany, September 21-25, 2015, Proceedings

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The Metric-FF Planning System: Translating ‘Ignoring Delete Lists’ to Numeric State Variables. Journal of Artificial Intelligence Research (JAIR) 20, 291–341 (2003) 9. : Introduction to Interval Analysis. Society for Industrial and Applied Mathematics (2009) 10. : The Algebra of Many-valued Quantities. O. fr 4 QCIS, University of Technology, Sydney, Australia Abstract. The paper proposes an oddness measure for estimating the extent to which a new item is at odds with a class. Then a simple classification procedure based on the minimization of oddness with respect to the different classes is proposed.

Obviously, the new bound does not originate in xk but the new interval xk+1 is obtained from (xk ◦e). The resulting interval depends on xk , xk , e, e and in case of division also on whether 0 ∈ e. Each combination of these extreme bounds is contained in one partial behavior T◦ (xk , e). If (xk ◦ e) hits a new behavior class or extends the bounds within a behavior class, this is a contradiction to succ◦ (x, e) being a fix-point. If (xk ◦ e) stays within a behavior, this is a contradiction to T◦ (xk , e) being well defined (Theorem 1).

We discuss several ways to extend this concept to numeric planning. Accumulation Semantics. In the accumulation semantics, instead of changing their values, variables accumulate all values achieved so far. The number of accumulated values after k parallel steps is finite, but generally exponential in k. Therefore, it quickly becomes infeasible to maintain the set of possible values, as can be seen in the task with o1 = ∅ → {x += 1} , o2 = ∅ → {x ÷= 2} and I(x) = 0. Denoting by xk , k = 0, . . , 3, the possible values of x after k parallel steps, we get x0 = {0}, x1 = {0, 1}, x2 = {0, 12 , 1, 2} and x3 = {0, 14 , 12 , 1, 32 , 2, 3}.

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