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186 Selected Studies on Software and Information Systems P 0 A 0 P 1 A 1 P 2 room(R1) room(R1) room(R1) at(Eva,R1) at(Eva,R1) at(Eva,R1) at(Adam,R1) at(Adam,R1) open(Adam,R1) at(Adam,R1) ¬ opened(R1) open(Adam,R1) ¬ opened(R1) move(Eva,R1,R2) ¬ opened(R1) man(Adam) man(Adam) opened(R1) move(Adam,R1,R2) man(Adam) opened(R1) at(Eva,R2) at(Adam,R2) Figure 6-13. Planning graph example. The following actions are available: open() Parameters: ?r, ?who Preconditions: (room ?r), not(opened ?r), (man ?who), (at ?who ?r) Effects: (opened ?r) move() Parameters: ?who, ?from ?to Preconditions: (at ?who ?from), (room ?from), (room ?to), (opened ?from) Effects: (at ?who ?to), not (at ?who ?from) In this example, the goal (Eva and Adam are in room R2) is achieved in the last, second layer. In [19] a mutex relation (Mutual Exclusions) between planning graph nodes is defined. The possible mutex relations between two action nodes are: inconsistent effects – one action negates the effect of the other, inference – one of the effect of one action negotiates a precondition of another, competing needs – one of the precondition of one action is mutually exclusive with the precondition of the other. In [19] also GRAPHPLAN is presented. It is an algorithm consisting of two stages altering in a loop: graph expansion and solution extraction. Graph expansion grows the graph until the reached proposition level includes goal propositions without mutex links. In solution extraction stage, backward-chaining strategy is used to look forward a potential plan. Backward Chaining One of the simplest methods used in web service composition is backward chaining. Its basic principle is to begin with the goal and search for services which produce its parts. This is used to reach the goal propositions and also the output. We take the set of goal propositions and look for services which produce them as effects. These services may have preconditions which are not satisfied. For this set of unsatisfied propositions we again look for services which produce them. This backward search is performed until we have service with unsatisfied preconditions. Similarly, this method is applied to get the required
Semantic Web Services 187 output. Here we look for services producing the required output data. The goal is to achieve a situation that each service has defined a source for its input data. If it is possible to compose a service from the available ones, backward chaining is a method which always finds it. Despite this, pure backward chaining is unusable effectively because of the computational complexity of this approach. Planning Based on Logical Programming There exist approaches which uses logical programming to solve the planning problem. In these, the planning problem is encoded as logic program composed by a set of Horn clauses. The plan is created using reasoning. In [75] it is shown that the planning problem can be converted to a logic program in linear time. Hierarchical Task Network Planning HTN (Hierarchical Task Network) provides a hierarchical abstraction to deal with the complexity of the planning problem. HTN like other planning techniques assumes a set of operators applicable when preconditions are satisfied to achieve defined effects. In addition it assumes also methods prescribing how tasks can be decomposed into subtasks. These prescriptions are a part of the domain knowledge. The decomposition is applied recursively until the resulting set of tasks consists only from executable ones. After each step of decomposition it is tested whether given conditions are violated. The goal is to achieve decomposition consisting of executable tasks without violating any given condition. A variant of HTN planning is an ordered task decomposition planning. Here the tasks are planned in the same order that they will be executed. This reduces the complexity of the planning problem. Temporal Planning Temporal planning is not a planning technique, but it rather refers to the ability of planners to deal with temporal aspects of the planning problem. It takes into the consideration the real duration of the actions and allows their parallel execution. Concerning also these aspects makes the problem more difficult but better reflects the real conditions of the problem that the planner solves. 6.3.3 Web Services Execution The result of the web service compositors is a plan describing a sequence of actions which need to be performed to achieve a goal. Each sequence is bound with a corresponding executable web service. To execute a composed web service an engine interpreting the composition is required [43, 44]. The execution can be performed in distributed manner [59]. In the case of services with high computational requirements, the execution is distributed in Grid environment. There exist approaches for description, construction and execution of Semantic web services in Grid [6, 36, 38]. Project K-Wf Grid 5 has brought results in this area. 5 http://www.gridworkflow.org/snips/gridworkflow/space/K-Wf+Grid/
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Edícia výskumných textov informa
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Štúdie vybraných tém programov
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vi Štúdie vybraných tém program
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viii Štúdie vybraných tém progr
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OBSAH DIEL I: SOFTVÉROVÉ PARADIGM
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Obsah xiii 9.3 Human-Computer Relat
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1 NÁVRHOVÉ VZORY Nikoleta Habudov
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Návrhové vzory 5 Z 23 vzorov sú
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Návrhové vzory 7 Použitie Uniká
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Návrhové vzory 9 Príklad z reál
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Návrhové vzory 11 − Konkrétna
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Návrhové vzory 13 public MMU crea
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Návrhové vzory 15 F’/R’ B Obr
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Návrhové vzory 17 Zvláštny výz
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Návrhové vzory 19 So vzorom Rukov
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Návrhové vzory 21 Problémy môž
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Návrhové vzory 23 používané, s
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Návrhové vzory 25 Vzor Farebné s
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Návrhové vzory 27 Obrázok 1-16.
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Návrhové vzory 29 Vytváranie a e
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Návrhové vzory 31 tmavšie miesta
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Návrhové vzory 33 kom (tzv. avata
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Návrhové vzory 35 [18] Schmidt, D
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38 Štúdie vybraných tém program
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100 Štúdie vybraných tém progra
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4 RÁMCE Ivan Kišac, Marián Šimk
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Rámce 117 − Rámec ierna skrinka
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Rámce 119 Výhody a nevýhody rám
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Rámce 123 Obrázok 4-5. Ukážka r
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Rámce 125 ziách tohto vývojovéh
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Rámce 127 Manažment transakcií.
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ZNOVUPOUŽITIE NÁVRHOVÝCH VZOROV
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Znovupoužitie návrhových vzorov
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EDÍCIA VÝSKUMNÝCH TEXTOV INFORMA
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