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Project Topics: MCI 2007.1. Jacques Robin. Topics Supervised by Prof. Jacques. Developing an ontology and component framework of search algorithms Top-level ontology classes derived from Russell & Norvig and Dechter Leaves of the ontology: to work with a Constraint Handling Rules (CHR)

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Topics supervised by prof jacques
Topics Supervised by Prof. Jacques

  • Developing an ontology and component framework of search algorithms

    • Top-level ontology classes derived from Russell & Norvig and Dechter

    • Leaves of the ontology:

      • to work with a Constraint Handling Rules (CHR)

      • to include variations of conflict-directed backjumping for complete global search

      • to include variations of min-conflict for incomplete, local, scalable search

    • Using UML knowledge representation and transformation rules from UML Components to Java OSGi Components

  • Incrementally developing multi-agent simulation

    • Starting from simplest simulation of penalty shot

    • Using UML as knowledge layer representation language

    • Using Java and CHR as implementation layer representation language



Framework top level

<<interface>>

StateSpaceSearch

+gSearch(StateSpaceSearchPb):SearchSolution

<<component>>

PruningHeuristic

+prune(Node):Node[*]

<<component>>

BtStrategy

+bt(Node):Node

<<interface>>

Cost2GoalHeuristic

+estimCost2Goal(Node):Real

<<interface>>

PruningHeuristic

+prune(Node):Node[*]

<<interface>>

BtStrategy

+bt(Node):Node

<<component>>

Cost2GoalHeuristic

+estimCost2Goal(Node):Real

<<component>>

StateSpaceSearch

+gSearch(StateSpaceSearchPb):SearchSolution

<<interface>>

ExpandStrategy

+choose(Fringe):Node

<<component>>

ExpandStrategy

+choose(Fringe):Node

StateSpaceSearchPb

+fullStateFornulation; Boolean

+suc(State,AgentAction):State

Path

+/cost:Real

Framework Top-Level

1..*

AgentAction

+name:String

+cost:Real

<<uses>>

<<uses>>

<<uses>>

<<uses>>

State

+full:Boolean

+goal:Boolean

+initial:Boolean

2..*

parent

models

child *

Node

+/expanded: Boolean

+/root: Boolean

+/visited: Integer

Fringe

*

NodeSolution

{ordered}

*

SearchSolution

PathSolution


Prof jacques search topic
Prof. Jacques’ Search Topic

VariableChoice

Heuristic

SearchProblem

SearchAlgo

PartialStateFormulation

SearchProblem

FullStateFormulationSearchProblem

GlobalSearchAlgo

LocalSearchAlgo

CSPSearchProblem

CSPSearchAlgo

FDCSP

SearchProblem

FDCSP

SearchAlgo

ValueChoice

Heuristic

PartialStateFormulation

FDCSPSearchProblem

FullStateFormulation

FDCSPSearchProblem

GlobalFDCSP

SearchAlgo

LocalFDCSP

SearchAlgo

Backtracking

Heuristic

CDBJ

Min-Confllict


Tasks
Tasks

  • Model UML2/OCL2 hierarchy of abstract and concrete specializations of StateSpaceSearchProblem (2 students)

    • including concrete classes and instances of:

      • 8 queens, CSP backjumping slides map coloring

  • Model UML2/OCL2 hierarchy of abstract and concrete specializations of CoastToGoalHeuristic (except for CSP problem, fully problem dependent) (same 2 students than 1)

  • Model UML2/OCL2 hierarchy of abstract and concrete specializations of ExpandStrategy and PruningHeuristic (Fúlvio)

  • Model UML2/OCL2 hierarchy of abstract and concrete specializations of BtStrategy (Zé Carlos, Renan)

  • Model UML2/OCL2 hierarchy of abstract and concrete specializations of StateSpaceSearch as assembly of abstract and concrete specializations ExpandStrategy, BtStrategy, PruningHeuristic, CoastToGoalHeuristic (Carlos, Alexandre)

  • Model UML2/OCL2 or other technology search visualization GUI(Joabe, speak to Luiz Lacerda, luiz.francisco.lacerda@gmail.com, about his UML2 Profile for GUI Modeling)


Tasks1
Tasks

  • Implementation OSGi Java, tests JUnit hierarchy of abstract and concrete specializations of StateSpaceSearchProblem (2 students)

    • including concrete classes and instances of:

      • 8 queens, CSP backjumping slides map coloring

  • Implementation OSGi Java, tests JUnit hierarchy of abstract and concrete specializations of CoastToGoalHeuristic (except for CSP problem, fully problem dependent) (same 2 students than 1)

  • Implementation OSGi Java, tests JUnit hierarchy of abstract and concrete specializations of ExpandStrategy and PruningHeuristic (Fúlvio)

  • Implementation OSGi Java, tests JUnit hierarchy of abstract and concrete specializations of BtStrategy (Zé Carlos, Renan)

  • Implementation OSGi Java, tests JUnit hierarchy of abstract and concrete specializations of StateSpaceSearch as assembly of abstract and concrete specializations ExpandStrategy, BtStrategy, PruningHeuristic, CoastToGoalHeuristic (Carlos, Alexandre)

  • Implementation OSGi Java, tests JUnit or other technology search visualization GUI(Joabe, speak to Luiz Lacerda, luiz.francisco.lacerda@gmail.com, about his UML2 Profile for GUI Modeling)


Scope search problems
Scope Search Problems

  • Priority1:

    • FullStateStateFormulation, PartialStateFormulation

    • CSPFullStateStateFormulation, CSPPartialStateFormulation

    • N-queens as FullStateStateFormulation

    • N-queens as PartialStateFormulation

    • 8-queens as FullStateStateFormulation

    • 8-queens as PartialStateFormulation

    • MapColoring as FullStateStateFormulation

    • R1-R7 MapColoring as FullStateStateFormulation

  • Priority 2:

    • PathSolutionProblem

    • ShortestPathBetween2Cities

    • Romenia


Scope expand and pruning strategies
Scope Expand and Pruning Strategies

  • General StateSpaceSearch

    • Priority 1:

      • For FullStateFormulationProblems: Depth-first, Backtracking

      • For PartialStateFormulationProblems: min-conflict

    • Priority 2: Uniform cost search, A*

    • Priority 3: Breadth-first, iterative deepening, RBFS

  • CSPSearch:

    • Priority 1:

      • Variable ordering: Degree Heuristic

      • Value ordering: Least Constraining Value

      • Pruning: forward checking

    • Priority 2:

      • Variable ordering: Minimum Remaining Value

      • Arc consistency


Scope backtrack strategies
Scope Backtrack Strategies

  • Chronological backtracking

  • Conflict-directed backjumping


Time table
Time Table

  • 09-13/07: Version 1.0 of first half of model

  • 16-20/07: Version 2.0 of first half of model

  • 30/07-03/08: Version 1.0 of first half implementation and 1.0 of second half of model

  • 13/08-17/08: Version 1.1 of first half implementation and 1.0 of second half implementation and integration tests

  • 22/08: Final report


Topic 2 starting point simplest possible multi agent simulation
Topic 2 Starting Point: Simplest Possible Multi-Agent Simulation

percept

Simulation

Agent

Shooter

Agent

Keeper

Agent

action

action

percept

gameOver, goal

action(s,legs,shoot(2))

action(k,legs,move(right))

actions(k,legs,move(right))

action(k,hands,grab(yes))

action(s,legs,shoot(3))

action(k,legs,move(right))

gameOver, nogoal


Topic 2 possible task division
Topic 2 Possible Task Division Simulation

  • Simulation Agent:

    • Reasoning

    • Simulation Visualization:

    • Agent Reasoning Explanation Visualization

  • Shooter Agent:

    • Reasoning

    • Simulation Visualization:

    • Agent Reasoning Explanation Visualization

  • Keeper Agent

    • Reasoning

    • Simulation Visualization:

    • Agent Reasoning Explanation Visualization


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