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מערכת החזיה וממשק משתמש גרפי לסימולטור בעיבוד אילוצים מבוזר

מערכת החזיה וממשק משתמש גרפי לסימולטור בעיבוד אילוצים מבוזר. אז מה אתם עושים בפרוייקט שלכם?. ניר הרשהורן וגיא רפפורט הנדסת תוכנה 2010/2011. Here’s an example CSP we all know:

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מערכת החזיה וממשק משתמש גרפי לסימולטור בעיבוד אילוצים מבוזר

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  1. מערכת החזיה וממשק משתמש גרפילסימולטור בעיבוד אילוצים מבוזר אז מה אתם עושים בפרוייקט שלכם? ניר הרשהורן וגיא רפפורטהנדסת תוכנה 2010/2011

  2. Here’s an example CSP we all know: • Place n (=4 in our case) Queens on a [n x n] Chess Board so that no queen is threatening/being threatened by another queen. • Constrains are obvious (in terms of Chesse). • Always solvable. Background The 4 Queens Problem

  3. A more complex form of CSP: Sudoku. • Always solvable? Depends on initial configuration! • CSP, like SAT, is NP-Hard: you can express any problem in terms of CSP. • COP (Constraint Optimization Problems) are also available and treated in our project, but we will not touch these during this presentation. Background Sudoku

  4. Backtracking example

  5. Backtracking example

  6. Backtracking example

  7. Backtracking example

  8. If all knowledge about the problem can be gathered into one agent, this agent could solve the problem alone using normal centralized constrained satisfaction alogrithms. However, Collecting all information about a problem requires not only communications costs but also the costs of translating one’s knowledge into an exchangeable format. For example, a constraint might be stored as a very complicated specialized internal function within an agent. In order to commnicate the knowledge of this constraint the another agent, which might be implemented on different computer architecture, the agent would have to translate the knowldege into an interexchangeable format, such as a table of allowed (or not allowed) combinations of variable values. These costs of centralizing all information to one agent could be prohibitively high. Background Distribution comes into Play (DisCSP)

  9. (So our first motivation was – that sharing information requires a common format, which is sometimes not so easy to achieve.) Furthermore, in some application problems, such as software agents in which each agent acts as a secretary of an individual, gathering all information to one agent is undesirable or impossible for security/privacy reasons. In such cases, multiple agents have to solve the problem without centralizing all information. –Makoto Yokoo, inventore of ABT Background Distribution comes into Play (DisCSP)

  10. Background Tuesday, 9AM? The Distributed Environment

  11. Background Fine by me! The Distributed Environment Tuesday, 9AM? Wednesday, 16:00?

  12. Background Tuesday, 9AM? Wednesday, 16:00? The Distributed Environment Fine with me! NO WAY!!! Tuesday, 9AM!

  13. Problem Domain • A CSP is defined by the tuple<X, D, R> • X: a finite set of variables (or agents) • D: Domain of each variable • R: Relations or Constraints between variable assignments. • Assignment: a <var,val> pair.. • Solution: a set of |X| assignments, which satisfies all relations. • Each of the different algorithms tries to solve more efficiently – minimizing constraint checks, assignments, and convering a minimal part of the search tree of the problem. • More at the BGU-DCR team’s page (cs/~dcr).

  14. Current Situation • The BGU-DCR team headed by Prof. Amnon Meisels is operating in the field of DCR for several years. • Group members find themselves developing personal frameworks for DCR over and over again. • A Simulator with GUI has been written but is now deprecated. • Recently, a new simulator was born out of the final CS project of Guy Yafe. • They’ve been toying with the idea of visualization for a couple of years – and this is where we enter.

  15. Proposed Solution • The project consists of two parts: • Developing a practical GUI for the existing simulator • May enhance usability! • World fame ensues? • Competing with current FRODO GUI • Developing visualization for DCR Algorithms • Appeals to user. • Allows researchers to ‘get the feel’ of different algorithms’ behaviour. • Great tool for debugging an implementation!

  16. Proposed Solution • The project consists of two parts: • Developing a practical GUI for the existing simulator • May enhance usability! • World fame ensues? • Competing with current FRODO GUI • Developing visualization for DCR Algorithms • Appeals to user. • Allows researchers to ‘get the feel’ of different algorithms’ behaviour. • Great tool for debugging an implementation!

  17. System Architecture + Technologies

  18. System Architecture + Technologies • Written in JAVA, which is preferred by our customers and is also the implementation language of the current simulator. • Using JUNG for Java graphs visualizations. • Contrasted and competing other DCR simulators (FRODO, Choco, et al).

  19. Main Functional Requirements • Visualizations • MEB • Message Delay Mechanism • Synchronization Of Runs • Structured Output Files

  20. Visualizations • Dynamically-built Search Tree • Inter-agent communication graph • Histograms Branch is part of assignment Assignment is currently checked Deprecated assignment Old failed assignments will fadeand disappear.

  21. Visualizations • Dynamically-built Search Tree • Inter-agent communication graph • Histograms A message is currently sent from A2A1. These two have a rather recent messages history. {A1,A3} also have profound message history butthey link was inactive for a long time. A1 A2 A3

  22. Visualizations • Dynamically-built Search Tree • Inter-agent communication graph • Histograms Different Measurements. For instance: number of assignments, number of messages sent, number of consistency-checks performed, etc.! Agents A1 A2 A3 A4 A5 A6 A7 A8

  23. Main Non-Functional Requirements • Based on Guy Yafe’s existing simulator (integration must be seamless). • Implement More Algorithms:DPOP, O-DPOP, BnB-ADOPT, AFC, AFB-BJ • Tutorial (as opposed to FRODO!) • Open Source, hosted at SourceForge, observable by all peers around the world.

  24. Major Use-Cases

  25. Project Risks • “Risk” is coincidentally the middle name of both of us! Here are some risks we might encounter: • Integration with existing simulator. • Dependency upon existing Graph packages. • Visualization overload on Problem Solving. • Inevitable loss of execution information. • Visualization rate limited to human capabilities: algorithm executes numerous LIPS! • Writing to file in real time.

  26. אם אין שאלות נוספות – נותר לנו רק לאחל לנו ולכולכם וכמובן – חג אורים שמח לכל בית ישראלגיא וניר – http://www.cs.bgu.ac.il/~guyrap/visu

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