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Working Notes of the H'96 Workshop on Agent-Oriented Programming and Distributed Systems (ed. K. Fischer), DFKI Document D-96-06. DFKI. Busetta, P. et al. (2000) Structuring BDI agents in functional clusters. In Intelligent Agents, W. Proceedings of the 6th International Workshop on Agent Theories, Architecfures and Languages, ATAL-99 (eds N. Jennings and Y. Lesperance), LNAI Volume 1757. Lecture notes in Artificial Intelligence, pp. 277-289. Springer, Berlin. Bylander, T. (1994)The computational complexity of propositional STRIPS planning. Artificial Intelligence, 69(1-2), 165-204. Carnrnarata, S., McArthur, D. and Steeb, R. (1983) Strategies of cooperation in distributed problem solving. In Proceedings of the 8th International Joint Conference on Artificial Intelligence (IJCAI-83),Karlsruhe, Federal Republic of Germany, pp. 767-770. Carriero, N. and Gelernter, D. (1989) Linda in context. Communications o f the ACM, 32(4), 444-458. Castelfranchi, C. (1990) Social power. In DecentralizedAI - Proceedings of the 1st European Workshop on Modelling Autonomous Agents in a Multi-Agent World (MAclMAW-89)(eds Y . Demazeau and J.-P. Miiller), pp. 49-62. Elsevier, Amsterdam.

5,000 4,145 3,844 3,751 3,013 2,737 2,466 2,200 2,091

Since we are in H;(K,), either the full H1(K7,)-norm or the equivalent H 1 ( K T n ) seminorm can be used. The fact that the standard vertex interpolation is combined with the orthogonal projection on higher-order subspaces is why one speaks about projection-based interpolation.

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The details of measurement parameter con guration depend on the measurement method used; a case study speci c to active measurements is described below. Information common to all methods is the frequency of measurement and the location of measurements. CP needs not be co-located with actual measurement entities. The form of reported QoS data may also be con gured. For instance, QA may con gure a number of QoS classes into CPs, with CP transmitting only QoS classi cation to save bandwidth. In this case, QoS class limits are de ned for delay, delay jitter, packet loss percentage, and loss correlation. After a measurement, a CP returns a classi cation for each of the four quantities. Alternatively, a full QoS report may be transmitted, consisting of numerical estimators for delay, delay variation, packet loss, and packet loss correlation. QoS modelling used in the demonstrator is based on computing per-route QoS averages. In computing the averages, discrete time exponential moving average is used, i.e.: Et = Xt + (1 )Et 1 , (7.1)

For more information on PDF Packages, see 12. For more on tiling views see Tile later in this chapter.

g Back in the Layers palette, click the Dune Grass layer to make it active, and then choose Layer New Adjustment Layer Levels.

n Amount controls the amount of adjustment. Higher values in Shadows lighten the shadows more. Higher values in Highlights darken the highlights more. n Tonal Width controls the range of tones that are affected by changes in amount. Higher settings in Shadows affect a larger range of shadow tones. Lower settings affect a narrower range. The opposite happens when adjusting Tonal Width in the Highlights; higher values affect larger ranges of highlight tones, and lower values affect smaller ranges of tones. n Radius works like this: When the Shadows/Highlights command looks at an image, it decides whether a pixel is a shadow or a highlight by evaluating its surrounding pixels. The Radius slider allows us to fine tune this decision-making. Lower values restrict the area that is looked at when evaluations are made, and higher settings expand that range. Too large of a setting darkens or lightens the entire image rather than the area you are working on.

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