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[17] Clark, A., 2006, Early warning decision support system for the national disaster warning center, Thailand, International Disaster Reduction Conference, Early warning-development, implementation and Innovation, August 31, 2006, Davos, Switzerland. Retrieved July 3rd, 2008 from :
http://davos2006.idrc.info/Presentations/CL
ARK_A_Pres.ppt.pdf |
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the fastest. Since API (LSPI) is not guaranteed to converge, we ran it for at most 20 iterations, which was an upper bound on the number of iterations of OAPI. The results demonstrate that ABP minimizes the L∞ Bellman residual much more consistently than the other methods. Note, however, that all the considered algorithms would perform significantly better given a finer approximation.
## 9. Conclusion And Future Work
We proposed and analyzed approximate bilinear programming, a new value-function approximation method, which provably minimizes bounds on policy loss. ABP returns the optimal approximate value function with respect to the Bellman residual bounds, despite being formulated with regard to transitive-feasible value functions. We also showed that there is no asymptotically simpler formulation, since finding the closest value function and |
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Marek Petrik and Shlomo Zilberstein. Constraint relaxation in approximate linear programs.
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Supriya Raheja ,Assistant Professor in ITM University.She had done her engineering from Hindu college of Engineering,Sonepat and masters from Guru Jambeshwar University of Science and Technology,Hisar.
Smita Rajpal,ITM University .She is a researcher in the field of Soft Computing.Published various papers in International Journals and Conferences.Also serving many International journals and conferences as a reviewer/ committee member /Editrorial Board member.
110 |
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[11] S. Shimizu, P. O. Hoyer, A. Hyv¨arinen, and A. Kerminen, **A linear non-gaussian acyclic**
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a causal ordering in a linear non-gaussian acyclic model, Proc. of the 25th Ann. Conf. on Uncertainty in Artificial Intelligence (UAI09), 2009, pp. 506–513.
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14 |
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- *alarms* when specific environmental or clinical conditions are detected;
- *notifications* when the system receives new input or terminates the inference process;
- *reminders* according to an agenda.
The main difference between a suggestion and an alert is that the second is triggered by the identification of a specific behavior and may generate an immediate action as an output (e.g. a blinking light to indicate that there is a call), while the first is purely based on the medical knowledge encoded in the system and gives a |
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Tonelli, M. R. 2001. The limits of evidence-based medicine. *Respir Care 46,* 12 (December), 1435–40.
Wood, A., Virone, G., Doan, T., Cao, Q., Selavo, L., Wu, Y., Fang, L., He, Z.,
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Yamamoto, S., Mogi, N., Umegaki, H., Suzuki, Y., Ando, F., Shimokata, H., and Iguchi, A. 2004. The clock drawing test as a valid screening method for mild cognitive impairment. *Dementia and Geriatric Cognitive Disordorders 18*, 172–179.
Yesavage, J., Brink, T., Rose, T., Lum, O., Huang, V., Adey, M., and Leirer, V. 1982-1983. Development and validation of a geriatric depression screening scale: A
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[5] J. Cloutier, K.L. Nyman, and F.E. Su. Two-player envy-free multi-cake division. Mathematical Social Sciences**, 59(1):26 - 37, 2010.**
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Toby Walsh NICTA and UNSW
Sydney, Australia Email: [email protected] |
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are shifted slightly down (resp., up). From Figure 5 we can see that the average number of blocking pairs decreases very fast, reaching 5 blocking pairs after only 100 steps. Then, after 300-400 steps, we reach 0 blocking pairs (i.e. a stable marriage) almost all the times for all values of p1. Considering Figure 6, we can see that the algorithm starts with more singles for greater values of p1. This happens because, with more incompleteness, it is more unprobable for a person to be acceptable. However, after 200 steps, the average number of singles becomes very small no matter the incompleteness in the problem. Looking at both Figures 5 and 6, we observe that, although we set a step limit s = 50000, the algorithm reaches a very good solution after just 300-400 steps. In fact, after this number |
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Table 1. Comparison of classifiers
| SVM Classifier | Kappa |
|------------------|---------|
| Linear | 0.7806 |
| RBF | 0.9198 |
| Quadratic | 0.8378 |
| Ensemble | 0.8929 |
|
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than simply vp > vp ). Furthermore, we have also a property of respect for unanimity (Theorem 3.11): if a particular proposition is accepted by every individual, and the individual judgments are consistent, then that proposition is also accepted by the collective judgment. Another good property is the monotonicity given by Theorem 3.14. |
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## 48 · Jos´E J´Ulio Alferes Et Al.
Swift, T. **1999. A new formulation of tabled resolution with delay. In** *Recent Advances in Artifiial* Intelligence**. LNAI, vol. 1695. Springer, 163–177.**
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Tarski, A. **1955. Lattice-theoretic fixpoint theorem and its applications.** Pacific Journal of Mathematics 5, **2, 285–309.**
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van Gelder, A., Ross, K. A., and Schlipf, J. S. **1991. The well-founded semantics for general**
logic programs. *Journal of the ACM 38,* **3, 620–650.** |
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For certain scenarios higher number of ties may be considered weakness of a decision measure. This helps to choose between Γ1, Γ2 and Γ3. **Following is a count of ties in a**
sample run of the simulation:
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[12] A. Skowron, C.M. Rauszer, **The implementation of algorithms based on discernibility**
matrix, **Manuscript (1991).**
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Knowledge-Based Systems 21(8) (2008) 941–945. |
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International Journal of Artificial Intelligence & Applications (IJAIA), Vol.1, No.3, July 2010 June 28 - July 2, 1998, Proceedings**, Lecture Notes in Computer Science, A. J. Hu and M. Y.**
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[30] U. Stern and D. L. Dill, "Improved probabilistic verification by hash compaction", in *CHARME*
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[31] D. Lee, "Design and verification of the Mars exploration rover primary payload" in **Proceedings**
of Workshop on Spacecraft and Launch Vehicle Dynamic Environments, 2003.
29 |
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[6] F. Smarandache, J. Dezert, **Advances and applications of DSmT for information fusion (Collected**
works)**, Vols. 1-3, American Research Press, 2004–** 2009. http://www.gallup.unm.edu/~**smarandache/DSmT.htm**
Page 26
[7] F. Smarandache, J. Dezert, J.-M. Tacnet, **Fusion**
of sources of evidence with different importances and reliabilities**, accepted in Fusion 2010 conf, Edinburgh, July 26-29, 2010.**
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the Transferable Belief Model**, IEEE Trans. PAMI** 12, pp. 447–458, 1990.
[9] J. Sudano, **The system probability information**
content (PIC) relationship to contributing components, combining independent multi-source beliefs, hybrid and pedigree pignistic probabilities**, Proc. of** Fusion 2002, Vol.2, pp. 1277-1283, Annapolis, MD,
USA, July 2002. |
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Table 5: The ranks of the embedded tile for varying size k **in the rankings returned by our compression ratio method and the tiling method from Geerts et al**
(2004). Note that for tile size 5, no results are available for the Pubmed database as the support threshold there is 10 such that the embedded tile is not retrieved.
Clearly, the newly proposed method is much more effective at ranking **the embedded tile highly.**
k 5 10 15 20
ICDM **Compression ratio method** 3 1 1 1
Tiling databases > 100 71 16 3
KDD **Compression ratio method** 2 1 1 1
Tiling databases > 100 94 19 5
Pubmed Compression ratio method N/A 1 1 1
Tiling databases N/A > 100 76 19
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IEEE proceedings of the 5th International Conference on Signal-Image Technology and Internet based Systems, Marakesh, November, 2009 use machine to do normal knitting. So French textile Institute develops expertise on cylindrical (ot Integral) knitting in normal (no cylindrical) machine.
2. A capitalization of knowledge process with MASK method is applied and knowledge is represented as: process, problem solving, constraints and concepts models. Graphical representation is <image>
3. Knowledge so represented is then transformed in training schemas:
Learners are conducted to follow a course based on the experts' process of knitting. For each step: they have to solve a problem and follow the main steps of experts' strategy (Figure 5. ). |
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27. Watts, D., Strogatz, S.: Collective dynamics of 'small-world' networks. Nature 393, 440–442 (1998)
28. Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann (2005)
29. Xu, L., Hutter, F., Hoos, H., Leyton-Brown, K.: SATzilla: Portfolio-based algorithm selection for SAT. J. Artif. Intell. Res. (JAIR) 32, 565–606 (2008) |
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15. Sanders, P.: Better algorithms for parallel backtracking. In: Workshop on Algorithms for Irregularly Structured Problems. pp. 333–347 (1995)
16. Schulte, C.: Parallel search made simple. In: Proceedings of TRICS. pp. 41–57
(2000)
17. Yokoo, M., Durfee, E.H., Ishida, T., Kuwabara, K.: Distributed constraint satisfaction for formalizing distributed problem solving. In: 12th IEEE International Conference on Distributed Computing Systems. pp. 614–621 (1992)
18. Yokoo, M., Durfee, E.H., Ishida, T., Kuwabara, K.: The distributed constraint satisfaction problem: Formalization and algorithms. IEEE Trans. on Knowl. and Data Eng. 10(5), 673–685 (1998) |
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Zeeshan Ahmed (2009) Aero Fighter - 2D Gaming In: 9th National Research Conference on Management and Computer Sciences, SZABIST Institute of Science and Technology, Pakistan
[2] Eager vs. Lazy Sensing, Reviewed 26 March 2009,
<http://ai.eecs.umich.edu/cogarch2/prop/eager-lazy-sensing.html>
3 |
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Figure 2: Accuracy of isomorphic subgraph matching for NM and NM* methods.
Accuracy Accuracy
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Accuracy |
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3.0 ANALYSIS, DISCUSSION AND INTERPRETATION: **The procedure discussed in** HILL-DOES has been implemented in Java. The user supplies the details of the equation like number of variables involved, coefficients, powers and the value of N. **The experimental results**
have been analyzed and discussed in the following sections.
3.1 Nodes Generated: **Figure 2 shows the nodes generated by HILL-DOES for an elementary**
equation x 1 2 **+ x** 2 2 **= 149, before finding the first solution (10,4). The process generated 68**
nodes during the search process. The figure shows the steady search of the process in the search space. Figure 3 demonstrates the convergence of heuristic function values of the nodes generated in the same demonstration. Initially, there is a sudden reduction of heuristic function values and once the process becomes mature, there is a directed approach towards the value zero, finally resulting in the solution.
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pmax. These values are used as the boundaries of the region for the ideal transmission power levels.
Therefore, our scaling method consists in using Eq. 6 for obtaining p new min and p new max for delimiting the value of the new networks' ideal transmission power level.
With this scaling methodology we repeated the initial experiment (that is, without packet loss) for a range of different network sizes k ∈ [0, 300]. The graphic in Figure 4 (bottom) shows the evolution of the resulting mean system activity. Ideally we would have expected a more or less straight line at about 0.6. This would have meant that the introduced parameter scaling method leads to a system that behaves equally for all network sizes. However, as can be seen, the scaling method only works well for networks with more than 100 nodes. For smaller networks, the mean system activity decreases.
However, this can be explained by the decreasing connectivity and communication ability. |
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If one player has 6 or 7 dominoes a face of which is the same, then the player informs the situation and shows the stones, and then pieces are mixed again and re-apportioned. |
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## 3D Domino Pieces And The Game Table Xna Description
XNA is a library developed by Microsoft in order to develop games. Visual Studio works at C \#. It is more developed/advanced version of DirectX. This library contains many libraries such as graphics, sound library, etc. Games that are made in this library work in XBOX and PC. Input devices (keyboard, mouse, gamepad, game controller) are easily transmitted and the displayed card is reached through XNA. We can also control volume in XNA
## Camera
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Domino Design
Domino piece is a cube with two large faces/sides. One of them is the visible face and shows
<image> the domino numbers. The other side is not visible. Cube is formed from the triangles in XNA. The cube has eight corners and six faces. Each side consists of two triangles. |
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Slowinski, K.,Stefanowski, J, Swinski D (2002) "Application of rule induction and rough sets to verification of magnetic resonance diagnosis",*Fundamenta Informaticae*,53(3-4),345-363 Wakulicz-Deja, A and Paszek,P. (1997) "Diagnose progressive encephalopathy applying the rough set theory", *International Journal of Medical Informatics* 46,119-127 Yao, Y., Wong, .K.M. and Lin, T.Y.A. (1997) "Review of Rough sets models", In: Lin, T.Y.
and Cercone,N.,eds, *Rough Sets and Data Mining-Analysis for Imprecise Data* (Kluwe,Boston). Ziarko,W (1993) "Analysis of uncertain information in the framework of variable precision rough sets", *Fund. Computing Decision Science* 18,381-396 |
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Figure 11: Configurations of w 0 , w fs , w 5t , w 1 for clause c = p r V ¬p s V p t Combining with the constraints involving the spatial variable ν c , we are now ready to introduce Nc.
Definition 7. The basic CDC network N c for c = p x V p x V p x contains the basic CDC
constraints in N V (see Definition 6), and the basic CDC constraints used, explicitly or implicitly, in Eq.s 34-41.
Note two new parallel relations are introduced in Nc. The spatial variable set of Nc includes those in Nv, and vc, wc, wc, wc, wc, and two auxiliary variables for constructing parallel relations.
Proposition 3. Suppose N c is the basic CDC network defined for clause c ≡ p z ∨ p s ∨ p s ∨ p s . In any solution of Nc, if u x (u x , u x , resp.) is horizontally instantiated, then its mbr bridges the gap between M(w δ ) (M(w ε s ), M(w ε t ), resp.) and M(w ε s ) (M(w ε t ), M(w ε t ),
resp.). |
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Finger Print Face
Yan and Zang have proposed a correlation Filter bank based fusion for multimodal biometric system; they used this approach for Face & Palm print biometrics. In Correlation Filter Bank, the unconstrained correlation filter trained for a specific modality is designed by optimizing the overall original correlation outputs. Therefore, the differences between Face & Palm print modalities have been taken into account and useful information in various modalities is fully exploited. PCA was used to reduce the dimensionality of feature set and then the designed correlation filter bank (CFB) was used for fusion. Fig. III shows the fusion network architecture proposed by them, the recognition rates achieved are in the range 0.9765 to 0.9964 with the proposed **method**
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3.2. Normalization |
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We ran the Bayesian DT technique without *a priori* information on the preferable DT
shape and size. The minimal number of data points allowed in the splits, pmin, was set equal to 15 or 1.2% of the 1250 training examples. The proposal probabilities for the death, birth, change-split and change-rules were set to 0.1, 0.1, 0.2, and 0.6, respectively. The numbers of burn-in and post burn-in samples were set equal to 100k and 10k, respectively. The sampling rate was set equal to 7, and the proposal variance was set at 0.3 in order to achieve the rational rate of acceptance rate around 0.25, which was recommended in [5].
5 fold cross-validation was used to estimate the variability of the resultant DTs.
The performances of all the 5 runs were nearly the same, and for the first run Fig. 2 depicts samples of log likelihood and numbers of DT nodes as well as the densities of DT nodes for burn-in and post burn-in phases. |
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Are SNOMED CT Browsers Ready for Institutions? Introducing MySNOM 3
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CT-related documents. It uses different colors to emphasize several concepts:
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the main concept is marked in yellow and hierarchy relationships are colored in red. The diagramming module creates an intermediate representation of the browsed concept using the DOT language [6]. The DOT language is a plain text description that can be read by humans and processed by visualization tools. MySNOM uses Graphviz [7], an Open Source, fast, graph generation software, to generate the final diagram. Generated diagrams reflect the ontological nature of SNOMED CT, as a directed graph, showing all concepts and relationships of a given concept. A sample diagram is shown in Figure 3. |
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6. Eric Yu. Modelling Strategic Relationships for Process Reenginering. PhD thesis, |
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Pattern 1 ?cell:CLASS,
?anatomyPart:CLASS,
?partOfRestriction:CLASS = cell and part of some ?anatomyPart,
?anatomyIntersection:CLASS = createIntersection(?partOfRestriction.VALUES) BEGIN ADD ?cell equivalentTo ?anatomyIntersection END; Pattern 2
?participant:CLASS,
?participatesRestriction:CLASS = ?cell and participates in some ?participant,
?participatesIntersection:CLASS = createIntersection(?participatesRestriction.VALUES)
BEGIN
ADD ?cell SubClassOf ?participatesIntersection END;
Fig. 7. Two OPPL 2 patterns for describing cell types in KUPO
Class: kupo 000027 EquivalentTo: cell:CL 0000000 and (ro:part of some MA:MA 0002580)
SubClassOf:
cell:CL 0000000, ro:participates in some gene ontology:GO 0002000, ro:participates in some gene ontology:GO 0002001 Fig. 8. Manchester OWL syntax for Juxtaglomerular cell (MA 0002580 = 'part of afferent arteriole forming juxtaglomerular complex', GO 0002000 = 'detection of renal blood flow' and GO 0002001 = 'renin secretion into blood stream' |
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20. The Gene Ontology Consortium. Gene Ontology: Tool for the Unification of Biology. *Nature Genetics*, 25:25–29, 2000.
21. Katy Wolstencroft, Stuart Owen, Matthew Horridge, Olga Krebs, Wolfgang Mueller, and Carole Goble. RightField: Rich Annotation of Experimental Biology Through Stealth Using Spreadsheets. In *Proceedings of the 7th Microsoft* eScience Workshop, pages 117–119., Berkeley, California, 2010. |
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Figures 3, 4, 5 and 6 show that the prediction follows the real measures of the index of interest related to all the observed categories. We note that it eliminates sudden changes and smoothes the abrupt variations. These characteristics are those we are going to use in our strategy which focuses on macroscopic recommendations (see next section).
The Figure 7 represents the cosine distance calculated between the estimation data and the observed data. Most of estimated values are corrects, with a cosine distance inferior to 0,15. |
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The implication of the proposed capitalisation approach and EIKC system is that creation and exploitation of collaborative knowledge of actors in the course of handling DP resolution yield validated or reliable knowledge which is accessible to them based on their needs.
The second implemented system called MECOCIR handles the information process needed in resolving a decision problem. This system allows EI actors especially information watchers to engage in synchronous and asynchronous collaboration during information problem resolution phase. The actors can collaboratively suggest relevant information sources and as well search together for relevant information from |
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such sources. The three level of group awareness that we proposed are fully implemented in this system. All the knowledge produced in the course of the interactions among the actors are as well captured and capitalized for future reuse. Figure 11 shows the main interface of MECOCIR.
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```
respective timestamp such that previously captured knowledge is not replaced by new entry. Figure 4 illustrates this phase by showing the dynamic acquisition
at a timestamp, denoted by td for timestamp declaration and ta for annotation timestamp.
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3.1.2 Knowledge Representation
We represent knowledge resources with the aid of conceptual knowledge model. This models the properties of KR and the relationship among them. The
generic conceptual model depicted in figure 5 represents general case that is applicable to problem solving process in a domain.
```
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in Artificial Intelligence and Applications, pages 107–117, Valencia, Spain, August 2004. IOS Press. ISBN 1 58603 451 0.
9 |
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13 (1989) 1-49. [7] W. A. T. Wan Abdullah, "Biconditionality, Analogy, Induction and Predicate Logic", *Malaysian J.* Comput. Sci. 3 (1987) 21-28.
[8] W. A. T. Wan Abdullah, "Neural Network Logic", in O. Benhar, C. Bosio, P. del Giudice & E. Tabet
(eds.), *Neural Networks: From Biology to High Energy Physics*, ETS Editrice, Pisa, 1991, pp. 135-142. [9] W. A. T. Wan Abdullah, "Logic Programming on a Neural Network", *J. of Intelligent Systems* 7 (1992) 513-519. [10] J. J. Hopfield, "Neural networks and physical systems with emergent collective computational abilities", *Proc. Natl. Acad. Sci. USA* 79 (1982) 2554-2558.
[11] J. J. Hopfield & D. W. Tank, ""Neural" Computation of Decisions in Optimization Problems",
Biol. Cybern. 52 (1985) 141-152. [12] W. A. T. Wan Abdullah, "The Logic of Neural Networks", *Phys. Lett.* **176A** (1993) 202-206. |
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<image>
initial viewpoint *flex* 0B by:
$$\frac{f l e x_{B}^{0}\to f l e x_{B}^{1}\quad\lnot(s t u b_{B}\leftrightarrow f l e x_{B}^{0})}{f l e x_{B}^{1}}\ (W)$$
and he checks the provisional negotiation situation by:
A : flex 0A ¬(stubB ∧ τB,A(*flex* 0A))
B : absDis(A : flex 0A) ∩ B : *flex* 1B(AD)
Bob says to Alice that they are in absolute disagreement and makes a proposal *flex* 1B.
The system continues the MN by:
∗(A, B) A : *flex* 0A B : absDis(A : flex 0A) B : *flex* 1B
Negotiate(A, B)(N) |
|
# Universidad Politécnica De Valencia
Escuela Técnica Superior de Ingeniería Informática
<image>
<image>
Una arquitectura para la evaluación de sistemas inteligentes PROYECTO FINAL DE CARRERA
Realizado por: Javier Insa Cabrera Dirigido por: José Hernández Orallo Valencia, 01 de octubre de 2010 |
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## 5.5. Varios Movimientos De Los Agentes Generadores
Hasta el momento hemos realizado los experimentos teniendo en cuenta que los agentes generadores Good y Evil solo podían moverse de una casilla a otra adyacente, sin embargo estos agentes deberían poder moverse varias celdas simultáneamente. En estos experimentos hemos dado a los agentes Good y Evil la oportunidad de moverse realizando 2, 3 y 4 acciones simultáneas a través del espacio.
## 2 Movimientos Espacio Definido Manualmente - 8 Celdas
2 Movimientos - Espacio definido manualmente Número de celdas: 8 Número de acciones: 3 Good y Evil: Comportamiento aleatorio Visión gráfica
<image>
|
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# 3 Movimientos
## Espacio Definido Manualmente - 8 Celdas
3 Movimientos - Espacio definido manualmente Número de celdas: 8 Número de acciones: 3 Good y Evil: Comportamiento aleatorio Visión gráfica
<image>
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l |
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# 4 Movimientos
## Espacio Definido Manualmente - 8 Celdas
4 Movimientos - Espacio definido manualmente Número de celdas: 8 Número de acciones: 3 Good y Evil: Comportamiento aleatorio Visión gráfica
<image>
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- **Autoajustar la complejidad del entorno para cada sesión en función de los**
resultados obtenidos por el agente que se está evaluando: Para la evaluación correcta de los agentes se deberá poder autoajustar la complejidad de los entornos generados para cada sesión, siguiendo el algoritmo en [Hernandez-Orallo
& Dowe 2010].
- **Evaluar personas y animales.**
- **Evaluar sistemas de IA, como las variantes de AIXI [Veness et al 2009] o agentes**
con técnicas de Q-learning u otros. |
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- Página 106 — |
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<image>
En la siguiente imagen podemos ver el diagrama de clases simplificado del sistema.
## Principales Diferencias Entre El Marco Conceptual Y 4.2. La Implementación Realizada 4.2.1. Observaciones
Según el marco conceptual visto anteriormente, a los agentes se les proporcionan las observaciones como una tira de caracteres tal y como podemos ver en el apartado 3.6.3.
Sin embargo, para esta primera implementación, hemos optado por realizar una copia de la estructura del espacio que lo representa dentro del programa y facilitarles a todos los agentes esta misma copia. De este modo se les facilita a todos los agentes la observación del entorno dándoles a todos una misma visión del entorno. |
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En lo referente al agente observador podemos ver como en este espacio siempre obtiene una recompensa de 0.5. Esto es debido a que ambas celdas siempre están ocupadas por algún agente distinto al que se está evaluando, los cuales son o Good o *Evil*, y por lo tanto siempre deberá compartir la recompensa obtenida en cualquier celda. Debido a que las sesiones se han realizado en un espacio tan pequeño, el agente observador siempre puede ver al agente *Good***, y por lo tanto siempre irá a por su**
recompensa. Sin embargo, como ya se ha dicho antes, está recompensa se dividirá entre los dos agentes que ocupan la celda obteniendo siempre recompensas de 0.5. Por lo tanto, en este espacio tan pequeño, el agente observador siempre obtiene la mayor recompensa posible dando como resultado una recompensa media de 0.5. |
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Una vez que disponemos de un espacio con un mayor número de celdas, las recompensas que se encuentran en éstas no siempre deberán compartirse entre varios agentes, por lo tanto la esperanza media de recompensas será superior a 0.5. Si realizamos los mismos experimentos sin recolocar a los agentes Good y *Evil* **las**
recompensas obtenidas por el agente observador siempre resultan ligeramente superiores a cuando se realizaba el cambio de posiciones de Good y *Evil***. Este es debido a**
que el agente observador no debe volver a buscar al agente *Good* **tras cada relocalización** y, por lo tanto, no perderá la recompensa que va dejando al tratar de encontrarle. |
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## 5.3. **Entorno Sesgado**
A continuación veremos los resultados a experimentos realizados en el espacio definido de 4 celdas utilizando comportamientos distintos para el agente *Good* y para *Evil*.
## Entorno Sesgado 1
Entorno sesgado Número de celdas: 4 Número de acciones: 3 Good y Evil: Comportamiento aleatorio Visión gráfica
<image>
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En este caso el agente *Good* **siempre tratará de cambiar de celda, a menos que se**
encuentre con *Evil* **en cuyo caso uno de los dos tendrá que esperar en su celda, por lo que**
generalmente el agente no compartirá la recompensa con el agente *Good*.
Como podemos ver en ambos entornos existe un sesgo con respecto a los resultados obtenidos por el agente aleatorio, lo cual significa que no se cumplen las propiedades de un entorno balanceado. Esto es debido a que no se han introducido elementos complementarios en el entorno (el agente *Good* y el agente *Evil* **no se comportan**
exactamente igual), siendo predominante en cada uno de los experimentos uno de los elementos. |
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## 5.4. **Evaluación Social**
En este caso vamos a medir a varios agentes evaluándose simultáneamente en la misma sesión, por lo que podremos ver como se ven modificados los resultados de los distintos agentes al existir otros compitiendo por las recompensas durante el transcurso de las sesiones. En estos experimentos introduciremos a los agentes aleatorio y observador simultáneamente en el entorno.
En la tabla vemos los resultados por parejas, donde cada resultado de Aleatorio y de Observador será el resultado para cada uno al evaluarlos sobre las mismas sesiones.
## Espacio Definido Manualmente - 8 Celdas
Evaluación social - Espacio definido manualmente Número de celdas: 8 Número de acciones: 3 Good y Evil: Comportamiento aleatorio Visión gráfica
<image>
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Function unit(F[l]) 1. If there exists a clause ll1 l2 ... ln in F,
then remove the clause ll1 l2 ... ln and the literals l1, l2,…, ln from F.
2. If there exists a clausel l'1 l'2 ... l'k in F,
removel fromll
'1 l'2 ... l'k.
3. For 1≤ i ≤
n do unit(F[li]).
4. Return F.
Park J.D.. 2002. MAP complexity results and approximation methods. In *18th UAI*, 388-396.
Sang T., Beame P., and Kautz H.A.. 2005. Performing Bayesian inference by weighted model counting. In *20th AAAI*, 475482.
Porschen S.. 2005. On some weighted satisfiability and graph problems. In 31s *SOFSEM*, 278-287. Porschen S.. 2006. Counting all solutions of minimum weight exact satisfiability. In *6th CIAC*, 50-59.
Valiant G.. 1979. The complexity of computing the permanent. Theoretical Comput. Sci. 8(2): 189–201. |
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Proof. In order to make the formula F *true*, the clause xy must be set *true*. And since in each clause only one literal can be true, x=y. Thus, y is substituted byx and this makes x a (1, 1)-literals, (2, 1)-literals, or (2, 2)-literals, which can be removed by the two resolution principles. Therefore, x and y can be both removed in this case. □
<image>
## Helpful Functions
The subsection discusses some functions used for simplifying the formulae. The first function *Reduce(F*) in Figure 2 is to simplify the formula F by recursively executing the common literal principle, the resolution principles, and some standard reductions used by (Kulikov 2005). It takes the formula F as input and returns the reduced F and a set of R recording the eliminating sub-clauses. The reason why we use R is that when using the common literal principle, the number of literals in |
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<image>
[4] N. Christofides, A. Mingozzi, and P. Toth. *Combinatorial optimization***, chapter The vehicle routing**
problem, pages 315–338. Wiley, New-York, 1979.
[5] F. Li, B. Golden, and E. Wasil. A record-to-record travel algorithm for solving the heterogeneous fleet vehicle routing problem. *Computers & Operations Research***, 34:2734–2742, 2007.**
[6] M. Sevaux and M.J. Geiger. Inventory routing and on-line inventory routing file format. Available at http://www2.hsu-hh.de/logistik/research/irp/irp11.pdf, 2011. |
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## Biographies
<image>
Shobha Shankar received the B.E. degree in Electrical Engineering in 1994, M.Tech degree in Power Systems in 1997 from the University of Mysore, Mysore. She is working as Asst. Professor in the department of Electrical and Electronics Engineering, Vidyavardhaka College of Engineering, Mysore. She is pursing her Doctoral degree from Visvesvaraya Technological University, India in the field of Power Systems.
T. Ananthapadmanabha received the B.E. degree in Electrical Engineering in
<image>
1980, M.Tech degree in Power Systems in 1984 and Ph.D. degree in 1997 from University of Mysore, Mysore. He is working as Professor, Department of Electrical and Electronics Engineering, The National Institute of Engineering, Mysore. His research interest includes Reactive Power Optimization, Voltage Stability, Distribution Automation and AI applications to Power Systems. |
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The Line Flow indices are divided into five categories using Fuzzy Set
<image>
notations: very small index (VS), small index (S), medium index (M), high index (H), and very high index (VH). Fig. 5 shows the correspondence between the Line Flow index and the five linguistic variables. Fig. 6 shows the severity index for voltage profile and Line Flow index.
<image>
The fuzzy rules, which are used for evaluation of severity indices of bus voltage profiles and line flow indices, are given in Table 1.
<image>
The Criticality Index is obtained by adding the two severity indices as shown in Fig. 7. The Criticality Index is obtained at critical load for all the load buses.
The buses are ranked in decreasing order of Criticality Index.
The following are the steps involved in the approach: |
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## B1. Crossing The Xml File And Generation Of Instances
The Request Constructor Agent crosses the XML file to extract its arborescence (cf. Figure 6).
Then it sends these last ones to the rules base N°1, in order to know those representing classes and those represent attributes. So, the rule that uses the agent in this case is as follow: "If a tag possesses sub-tags then it is considered as a class, otherwise it is an attribute. Then by applying this rule, we obtain the names of classes with its attributes and also their values (instances)".
<image>
|
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N. DI MAURO, T.M.A. BASILE, S. FERILLI, AND F. ESPOSITO
<image>
taking fixed the neighbourhood operator to SPLIT/MERGE. The best values are obtained with a walk probability equal to 0.6 or to 0.7. There is an increasing improvement for values ranging from 0.2 to 0.6/0.7; then the quality of the found solutions starts to decrease for values ranging from 0.7 to 0.95.
14 |
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<image>
Path-relinking is an intensification strategy, proposed in [10], that explores trajectories connecting elite solutions obtained by tabu search [9] or scatter search [24].
Given a set of elite solutions, paths among elite solutions in the solution space are generated and traversed hoping to visit better solutions. Paths are generated 15 |
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<image>
the mean value of the iterations required by the GRASP+PR algorithm to end a single run.
In the second experiment of GRASP+PR we evaluated its efficacy by varying the size of the pool of elite solutions. Adopting the same setting of the last experiment, 18 |
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N. DI MAURO, T.M.A. BASILE, S. FERILLI, AND F. ESPOSITO
<image>
## 7. I Mplementation Details
7.1. The characteristic function. Concerning the representation of the characteristic function and the search space, given n agents N = { a1, a2,..., an}, we recall that the number of possible coalitions is 2 n - 1. Hence, the characteristic 20 |
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GRASP AND PATH-RELINKING FOR CSG
<image>
S C A (coalition) is described as a binary number c B = b 1 b 2 . . . b n where each b i = 1 if a i ∈ S i = 0 otherwise. For instance, given n = 4, the coalition { a2, a3} corresponds to the binary number 0110. Now, given the binary representation of a coalition S , its decimal value corresponds to the index in the vector CF where 21 |
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22 N. DI MAURO, T.M.A. BASILE, S. FERILLI, AND F. ESPOSITO
<image>
its corresponding value v(S**) is memorised. This gives us the possibility to have a** random access to the values of the characteristic functions in order to efficiently compute the value v **of a coalition structure.**
7.2. Coalition structure. Given a coalition structure C = {C1, C2, . . . , Ck}, assuming that the Ci are ordered by their smallest elements, a convenient representation of the CS is an integer sequence d1d2 · · · dn where di = j, if the agent ai belongs |
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grasp and path-relinking for csg
<image>
while those computed by IDP is
$$\mathbf{S}_{IDP}=\sum_{s=1}^{n}\mathbf{C}(n,s)\left(\sum_{k=\lceil s/2\rceil}^{s-1}S(s-k,k)\mathbf{1}_{\{k\leq n-s\lor s=n\}}\right),$$ where $\mathbf{1}_{\{k\leq n-s\lor s=n\}}$ is $1$ if $k\leq n-s$ or $s=n$, $0$ otherwise.
7 |
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<image>
Nm,n shows Neuron where, m = Layer Number & n = Neuron Number Wi,j,k represents Weight where, i=Layer No.., j=Neuron No., k=Output No.of the particular Neuron A Directional Feature with Energy based Offline Signature **Verification Network**
The proposed ANN scheme uses a multi layer feed forward network employing a back propagation learning algorithm with 16 Neurons in input layer and 1 Neuron in output layer. One hidden layer is present with 16 Neurons. The transfer function used for all the three layers are Hyperbolic Tangent Sigmoid (tansig). The proposed architecture of Neural Network is shown in Fig. 4. Here, default value of bias is chosen. Total 501 inputs i.e. 100 for energy of each segment, 400 as direction feature and 1 shows aspect ratio is given to this neural network. |
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<image>
<image>
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<image>
<image>
In Fig 1 It is seen that the bots starts traversing from any random initial point with any random battery strength. The bots keep exploring their environment in a predefined way. Once any one of the bot encounters a weak battery problem it stores its present position and moves to its docking point as shown in Fig 2 and Fig 3. On reaching its docking point it checks for the availability of the charger, if the charger is available then it moves to the charger and starts charging as shown in Fig 4 and Fig 5. Once the bot is charged it moves to its last stored working position as elaborated in Fig 5 and Fig 6. Considering more then two bots in their docking station and one in the charger, the first bot is considered as the next bot for charging as in FCFS described in Fig 7 and hence according to the Fig 7, bot 2 should be going in next to the charger. However in Fig 8, it is seen that bot 4 battery has |
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amount of depth-first behavior in an A∗-framework. This has the potential effect of delaying exploration of the ε-cost plateaus slightly, past the discovery of a solution, but still each planner is ultimately trapped by such plateaus before being able to find really good solutions. Then such tricks are mostly serving to mask the problems of cost-based search
(and ε-cost), as they merely delay failure by just enough that one can imagine that the planner is now effective (because it returns a solution where before it returned none). Using a size-based evaluation function more directly addresses the existence of cost plateaus, and not surprisingly leads to improvement over the equivalent cost-based approach - even with LAMA. |
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Control Theory and Informatics ISSN 2224-5774 (print) ISSN 2225-0492 (online)
Vol 1, No.2, 2011
## 3.5. Report Agent
<image>
Report agent delivers the final results to the user in a user friendly way by which user can perform his duties.
## 4. Working Of Vca
4.1. Pseudo code for Internal Agent The main steps of Pseudo Code for Internal Agent are given in the following box
<image>
## Checking By Internal Agent
i. If engine getting petrol and engine Turns over then problem is with the Spark plugs.
If engine not turn over and lights not come on then problem is with the battery.
ii.
ii. If engine not turn over and lights come on then problem is with the starter.
3 | P a g e ww.iiste.org |
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Morgan, N. and Bourlard, H. (1990). Generalization and parameter estimation in feedforward nets: some experiments. In *NIPS'89* , pages 413–416, Denver, CO. Morgan Kaufmann.
Nair, V. and Hinton, G. E. (2010). Rectified linear units improve restricted boltzmann machines.
In *ICML '10* . |
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[Rossi *et al.*, 2006] F. Rossi, P. van Beek, and T. Walsh, editors. *Handbook of Constraint Programming*. Elsevier, 2006.
[Simons *et al.*, 2002] P. Simons, I. Niemel¨a, and T. Soininen.
Extending and implementing the stable model semantics.
Artificial Intelligence, 138(1-2):181–234, 2002.
[Tamura *et al.*, 2006] N. Tamura, A. Taga, S. Kitagawa, and M. Banbara. Compiling finite linear CSP into SAT. In Proceedings of CP'06, pages 590–603. Springer, 2006.
[Walsh, 2000] T. Walsh. SAT v CSP. In *Proceedings of* CP'00, pages 441–456. Springer, 2000. |
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<image>
<image>
Contraction ratio |
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<image>
existentially quantied variables are shifted we get the st
and the ordering of universally quantied
, for each pair of universally quantied variables (
, we can see that each existentially quantied variable
because no universally quantied variable is between
is satisable. , a solution to the problem with the prex
<image>
quantied arc consistent, then P *is quantied arc consistent.*
) is quantied arc consistent in
is quantied arc consistent, any value in
, it is obvious that the quantied arc consistency holds. The
quantied arc consistent. |
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