CS483/683 Multi-Agent Systems Lecture 2: Distributed variants of 2 important AI problems: Search and Constraint Satisfaction 21 January 2010 Instructor:

Slides:



Advertisements
Similar presentations
Constraint Satisfaction Problems
Advertisements

Constraint Satisfaction Problems Russell and Norvig: Chapter
Local Search Jim Little UBC CS 322 – CSP October 3, 2014 Textbook §4.8
1 Today’s class Interleaving backtracking and consistency checking Variable-ordering heuristics Value-ordering heuristics Intelligent backtracking Marie.
Distributed Constraint Optimization Problems M OHSEN A FSHARCHI.
Lecture 11 Last Time: Local Search, Constraint Satisfaction Problems Today: More on CSPs.
1 Constraint Satisfaction Problems A Quick Overview (based on AIMA book slides)
1 CMSC 471 Fall 2002 Class #6 – Wednesday, September 18.
1 Constraint Satisfaction Problems. 2 Intro Example: 8-Queens Generate-and-test: 8 8 combinations.
MBD and CSP Meir Kalech Partially based on slides of Jia You and Brian Williams.
Stanford University CS243 Winter 2006 Wei Li 1 Register Allocation.
Constraint Satisfaction problems (CSP)
1 Traffic Engineering (TE). 2 Network Congestion Causes of congestion –Lack of network resources –Uneven distribution of traffic caused by current dynamic.
Review: Constraint Satisfaction Problems How is a CSP defined? How do we solve CSPs?
CPSC 322, Lecture 9Slide 1 Search: Advanced Topics Computer Science cpsc322, Lecture 9 (Textbook Chpt 3.6) January, 23, 2009.
4 Feb 2004CS Constraint Satisfaction1 Constraint Satisfaction Problems Chapter 5 Section 1 – 3.
Constraint Satisfaction Problems
Games with Chance Other Search Algorithms CPSC 315 – Programming Studio Spring 2008 Project 2, Lecture 3 Adapted from slides of Yoonsuck Choe.
CPSC 322, Lecture 12Slide 1 CSPs: Search and Arc Consistency Computer Science cpsc322, Lecture 12 (Textbook Chpt ) January, 29, 2010.
CS482/682 Artificial Intelligence Lecture 7: Genetic Algorithms and Constraint Satisfaction Problems 15 September 2009 Instructor: Kostas Bekris Computer.
Cache Placement in Sensor Networks Under Update Cost Constraint Bin Tang, Samir Das and Himanshu Gupta Department of Computer Science Stony Brook University.
Constraint Satisfaction Problems
Constraint Satisfaction Problems Russell and Norvig: Chapter 3, Section 3.7 Chapter 4, Pages Slides adapted from: robotics.stanford.edu/~latombe/cs121/2003/home.htm.
Constraint Satisfaction Problems
Search Algorithms for Agents
Chapter 5 Outline Formal definition of CSP CSP Examples
CS522: Algorithmic and Economic Aspects of the Internet Instructors: Nicole Immorlica Mohammad Mahdian
Distributed Scheduling. What is Distributed Scheduling? Scheduling: –A resource allocation problem –Often very complex set of constraints –Tied directly.
1 Adversary Search Ref: Chapter 5. 2 Games & A.I. Easy to measure success Easy to represent states Small number of operators Comparison against humans.
Variables “To be is to be the value of a variable.” - William Quine.
Escaping local optimas Accept nonimproving neighbors – Tabu search and simulated annealing Iterating with different initial solutions – Multistart local.
Distributed Constraint Optimization Michal Jakob Agent Technology Center, Dept. of Computer Science and Engineering, FEE, Czech Technical University A4M33MAS.
CS774. Markov Random Field : Theory and Application Lecture 13 Kyomin Jung KAIST Oct
1 Optimal Cycle Vida Movahedi Elder Lab, January 2008.
Stocs – A Stochastic CSP Solver Bella Dubrov IBM Haifa Research Lab © Copyright IBM.
Problem Reduction Search: AND/OR Graphs & Game Trees Department of Computer Science & Engineering Indian Institute of Technology Kharagpur.
Hande ÇAKIN IES 503 TERM PROJECT CONSTRAINT SATISFACTION PROBLEMS.
Artificial Intelligence CS482, CS682, MW 1 – 2:15, SEM 201, MS 227 Prerequisites: 302, 365 Instructor: Sushil Louis,
CSCI 5582 Fall 2006 CSCI 5582 Artificial Intelligence Fall 2006 Jim Martin.
1 2010/2011 Semester 2 Introduction: Chapter 1 ARTIFICIAL INTELLIGENCE.
Algorithmic, Game-theoretic and Logical Foundations
1 CSE 4705 Artificial Intelligence Jinbo Bi Department of Computer Science & Engineering
1 Constraint Satisfaction Problems Chapter 5 Section 1 – 3 Grand Challenge:
CHAPTER 5 SECTION 1 – 3 4 Feb 2004 CS Constraint Satisfaction 1 Constraint Satisfaction Problems.
Arc Consistency CPSC 322 – CSP 3 Textbook § 4.5 February 2, 2011.
Arc Consistency and Domain Splitting in CSPs CPSC 322 – CSP 3 Textbook Poole and Mackworth: § 4.5 and 4.6 Lecturer: Alan Mackworth October 3, 2012.
Chapter 5 Team Teaching AI (created by Dewi Liliana) PTIIK Constraint Satisfaction Problems.
Constraint Satisfaction Problems Russell and Norvig: Chapter 3, Section 3.7 Chapter 4, Pages CS121 – Winter 2003.
CS382 Introduction to Artificial Intelligence Lecture 1: The Foundations of AI and Intelligent Agents 24 January 2012 Instructor: Kostas Bekris Computer.
Chapter 9: Relationships Chapter 10: Inheritance and Polymorphism [Arlow and Neustadt, 2005] CS 426 Senior Projects in Computer Science University of Nevada,
1 Constraint Satisfaction Problems (CSP). Announcements Second Test Wednesday, April 27.
CSPs: Search and Arc Consistency Computer Science cpsc322, Lecture 12
Computer Science cpsc322, Lecture 13
CSPs: Search and Arc Consistency Computer Science cpsc322, Lecture 12
Constraint Satisfaction Problems Lecture # 14, 15 & 16
CprE 458/558: Real-Time Systems
Constraint Satisfaction Problems vs. Finite State Problems
CS 188: Artificial Intelligence
Computer Science cpsc322, Lecture 14
Games with Chance Other Search Algorithms
CSPs: Search and Arc Consistency Computer Science cpsc322, Lecture 12
Computer Science cpsc322, Lecture 13
CSP Search Techniques Backtracking Forward checking
Constraint Satisfaction Problems
CO Games Development 1 Week 8 Depth-first search, Combinatorial Explosion, Heuristics, Hill-Climbing Gareth Bellaby.
Constraint satisfaction problems
Constraint Satisfaction Problems
CS 8520: Artificial Intelligence
Constraint Graph Binary CSPs
Constraint satisfaction problems
Presentation transcript:

CS483/683 Multi-Agent Systems Lecture 2: Distributed variants of 2 important AI problems: Search and Constraint Satisfaction 21 January 2010 Instructor: Kostas Bekris Computer Science & Engineering, University of Nevada, Reno

CS483 Search on a Graph Consider a weighted directed graph n nodes N directed links L a weight function w: L → R + a start node s ∈ N a goal node t ∈ N or more generally a set of goal nodes T ⊂ N a b c d st Objective: Find a directed path from s to t having minimal total weight. Difference from typical AI view of the problem: Distributed solutions Each node performs only a local computation Each node has access only to the state of its neighbors

CS483 Distributed Path Planning procedure ASYNCH DP if i is a goal node then h(i) ← 0 else h(i) ←∞ repeat forall neighbors j do f(j) ← w(i,j) + h(j) h(i) ← min j f(j) a b c d st ∞ ∞ ∞∞ ∞ 0 a b c d st ∞ ∞ ∞1 3 0 a b c d st ∞

CS483 Learning Real-Time A* a b c d st procedure LRTA* i ← s while i is not a goal node do foreach neighbor j do f(j) ← w(i,j) + h(j) i’ ← argmin j f(j) h(i) ← max(h(i),f(i’)) i ← i’

CS483 Learning Real-time A* a b c d st a b c d st a b c d st a b c d st

CS483 Learning Real-Time A* - Multiple Agents a b c d st a b c d st a b c d st

CS790E Distributed Constraint Satisfaction Example: Sensor Networks Multiple processing units: local sensing capabilities limited processing power limiter power supply limited communication bandwidth Must provide, however, a global service as a team What algorithm should each agent run so that the center can still piece together a reliable global picture?

CS790E Running example: 3 sensors Three sensors with overlapping coverage regions Each sensor can choose 1 of 3 possible frequencies All frequencies work well as long as no two sensors with overlapping coverage areas share the same frequency Sensors have to decide frequencies for themselves Abstraction: Graph Coloring Problem Nodes: Sensors Frequencies: Colors Edges: If corresponding nodes share coverage area Choose a color for each node so that no 2 neighboring nodes have same color {red, blue, green} ≠≠ ≠

CS790E Distributed CSP Two types of algorithms 1. A least-commitment approach: Domain Pruning  Rule out impossible values without losing any possible solutions 2. More exploratory techniques: Heuristic Search  Select tentative variable values, backtracking when these choices prove unsuccessful