Download Advances in Knowledge Representation, Logic Programming, and by Thomas Eiter, Hannes Strass, Miroslaw Truszczyński, Stefan PDF

By Thomas Eiter, Hannes Strass, Miroslaw Truszczyński, Stefan Woltran

This Festschrift is released in honor of Gerhard Brewka at the party of his sixtieth birthday and comprises articles from fields reflecting the breadth of Gerd's paintings. The 24 medical papers integrated within the ebook are written by way of shut neighbors and associates and canopy issues similar to activities and brokers, Nonmonotonic and Human Reasoning, personal tastes and Argumentation.

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Advances in Knowledge Representation, Logic Programming, and Abstract Argumentation: Essays Dedicated to Gerhard Brewka on the Occasion of His 60th Birthday

This Festschrift is released in honor of Gerhard Brewka at the celebration of his sixtieth birthday and comprises articles from fields reflecting the breadth of Gerd's paintings. The 24 clinical papers integrated within the ebook are written through shut acquaintances and associates and canopy issues comparable to activities and brokers, Nonmonotonic and Human Reasoning, personal tastes and Argumentation.

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Extra resources for Advances in Knowledge Representation, Logic Programming, and Abstract Argumentation: Essays Dedicated to Gerhard Brewka on the Occasion of His 60th Birthday

Example text

Tcell_tile (X , Y ). tcell_tile (X , Y ). ncell_tile (X , Y ). ncell_tile (X , Y ). = empty . = empty . = empty . Put in words, a projected state requires a tin can at location (x, y) if, and only if, the corresponding cell in the 15-puzzle exists and is not empty. Combined with the action theories of Fig. 2 and 3 and augmented by the general clauses 5–12 from above, the resulting ASP does admit stable models. For instance, one model of the ASP includes true(cell (1, 1, empty)), true(cell (1, 2, empty)), legal(move(1, 1, 1, 2)) 42 M.

C t l . s o l v e ( on model = s e l f . on model ) return self . last solution 26 27 28 29 30 31 32 33 34 d e f o n m o d e l ( s e l f , model ) : s e l f . l a s t s o l u t i o n = model . a t o ms ( ) self . last positions = [] f o r atom i n model . ATOMS ) : i f ( atom . name ( ) == ” p o s ” and l e n ( atom . a r g s ( ) ) == 4 and atom . a r g s ( ) [ 3 ] == s e l f . h o r i z o n ) : s e l f . l a s t p o s i t i o n s . append ( Fun ( ” p o s ” , atom . a r g s ( ) [ : − 1 ] ) ) 36 37 38 39 40 41 42 43 44 horizon = 15 encodings = [” board .

X. Y. X. Y. 31 32 next ( step ( Y )) : - true ( step ( X )) , succ (X , Y ). 33 34 succ (1 ,2). succ (2 ,3). succ (79 ,80). Fig. 2. The 15-puzzle described by an action theory • cell (u, v) becomes empty while the tile is now in (x, y) (clauses 24, 25); • all other cells retain their tiles (clauses 27–30); • the step counter is incremented (clause 25). As can be seen from this example, our action theory uses the following unary predicates as pre-defined keywords: – – – – – init(f ), to define fluent f to be true initially; true(f ), denoting the condition that f is true in a state; does(a), denoting the condition that a is performed in a state; legal(a), meaning that action a is possible; next(f ), to define the fluents that are true after an action is performed.

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