Agent-Centered Search

1
Agent-Centered Search

• Mitja Luštrek
• Jožef Stefan Institute
• Department of Intelligent Systems

2
Introduction

• Setting mobile agent (robot) in an known/unknown
  environment (labyrinth with/without map)
• Objective to reach the goal from the starting
  position in as short time as possible
• Two phases
• Planning of the path
• Execution of the plan
• Traditional search first planning of the whole
  path, then execution of the plan
• Agent-centered search planning of the beginning
  of the path from the starting position, execution
  of the partial plan, planning from the new
  starting position...

3
Why Agent-Centered Search

• Planning long in comparison to execution
• Environment very large
• Environment not wholly known
• Environment changing
• Agent must act in real time
• Results
• Shorterplanning
• Longerexecution(path notoptimal)
• Shortersum

4
Traditional Search A

• Multiple paths from the starting position
• Agent keeps expanding the most promising path
  until the goal is reached
• Evaluation function for path ending in position
  n
• f (n) g (n) h (n)
• g (n) ... the length of the shortest path found
  so far from the starting position to n
• h (n) ... heuristic evaluation of the length of
  the shortest path from n to the goal
• If h (n) is admissible (optimistic always
  smaller or equal to the length of the shortest
  path from n to the goal), A finds the shortest
  path

5
A Example

• The agents environment is divided into squares,
  some of them impassable
• The agent can move up, down, left and right
• The distance between adjacent squares is 1
• h (n) is the Manhattan distance from n to the
  goal

6
A Example
4 3 2 1 0 GOAL
5 START 4 3 2 1
6 5 4 3 2
7 6 5 4 3
8 7 6 5 4
7
A Example
41 3 2 1 0 GOAL
5 START 4 3 2 1
61 5 4 3 2
7 6 5 4 3
8 7 6 5 4
8
A Example
41 32 2 1 0 GOAL
5 START 4 3 2 1
61 5 4 3 2
7 6 5 4 3
8 7 6 5 4
9
A Example
41 32 23 1 0 GOAL
5 START 4 3 2 1
61 5 4 3 2
7 6 5 4 3
8 7 6 5 4
10
A Example
41 32 23 1 0 GOAL
5 START 4 34 2 1
61 5 4 3 2
7 6 5 4 3
8 7 6 5 4
11
A Example
41 32 23 1 0 GOAL
5 START 4 34 2 1
61 5 45 3 2
7 6 5 4 3
8 7 6 5 4
12
A Example
41 32 23 1 0 GOAL
5 START 4 34 2 1
61 5 45 3 2
72 6 5 4 3
8 7 6 5 4
13
A Example
41 32 23 1 0 GOAL
5 START 4 34 2 1
61 5 45 3 2
72 6 5 4 3
83 7 6 5 4
14
A Example
41 32 23 1 0 GOAL
5 START 4 34 2 1
61 5 45 36 2
72 6 56 4 3
83 7 6 5 4
15
A Example
41 32 23 1 0 GOAL
5 START 4 34 2 1
61 5 45 36 27
72 6 56 47 3
83 7 6 5 4
16
A Example
41 32 23 1 0 GOAL
5 START 4 34 2 18
61 5 45 36 27
72 6 56 47 38
83 7 6 5 4
17
A Example
41 32 23 1 09 GOAL
5 START 4 34 2 18
61 5 45 36 27
72 6 56 47 38
83 7 6 5 4
18
A Example
41 32 23 1 09 GOAL
5 START 4 34 2 18
61 5 45 36 27
72 6 56 47 38
83 7 6 5 4
19
A Example
41 32 23 1 09 GOAL
5 START 4 34 2 18
61 5 45 36 27
72 6 56 47 38
83 7 6 5 4
20
Agent-Centered Search

• Agent searches local search space, which is a
  part of the whole space centered on the agent
• Makes some steps in the most promising direction
• Repeats until it reaches the goal
• In game playing (chess), the search is performed
  around the current position
• The whole game tree is too large (environment
  very large)
• The opponents moves are not known in advance
  (environment not wholly known)
• This is an example of two-agent search, we focus
  on single-agent search.

21
LRTA

• Learning real-time A
• Agent updates h (l) for every point l in the
  local search spaceh (l) min (d (l, n) h
  (n))
• d (l, n) ... the length of the shortest path from
  l to a point n just outside the local search
  space
• h (n) ... heuristic evaluation of the length of
  the shortest path from n to the goal
• Moves to the adjacent position l with the lowest
  h (l).
• Repeats until the goal is reached.
• Updated h (l) can be used in later searches.

22
LRTA Example

• Same setting as for A.
• The local search space is 3 x 3 squares centered
  on the agent.

23
LRTA Example
4 START 3 2 1 0 GOAL
5 4 3 2 1
6 5 4 3 2
7 6 5 4 3
8 7 6 5 4
24
LRTA Example
4 START 3 2 1 0 GOAL
5 4 3 2 1
6 5 4 3 2
7 6 5 4 3
8 7 6 5 4
25
LRTA Example
8 START 7 6 1 0 GOAL
7 6 5 2 1
6 5 4 3 2
7 6 5 4 3
8 7 6 5 4
26
LRTA Example
10 START 11 12 1 0 GOAL
9 10 11 2 1
8 9 10 3 2
7 6 5 4 3
8 7 6 5 4
27
LRTA Example
10 START 11 12 1 0 GOAL
9 10 11 2 1
8 9 10 3 2
7 6 5 4 3
8 7 6 5 4
28
LRTA Example
10 START 11 12 1 0 GOAL
11 12 11 2 1
10 11 10 3 2
9 6 5 4 3
8 7 6 5 4
29
LRTA Example
10 START 11 12 1 0 GOAL
11 12 11 2 1
10 11 10 3 2
9 6 5 4 3
8 7 6 5 4
30
LRTA Example
10 START 11 12 1 0 GOAL
11 12 11 2 1
10 11 10 3 2
9 6 5 4 3
8 7 6 5 4
31
LRTA Example
10 START 11 12 1 0 GOAL
11 12 11 2 1
10 11 10 3 2
9 6 5 4 3
8 7 6 5 4
32
LRTA Example
10 START 11 12 1 0 GOAL
11 12 11 2 1
10 11 10 3 2
9 6 5 4 3
8 7 6 5 4
33
LRTA Example
10 START 11 12 1 0 GOAL
11 12 11 2 1
10 11 10 3 2
9 6 5 4 3
8 7 6 5 4
34
LRTA Example
10 START 11 12 1 0 GOAL
11 12 11 2 1
10 11 10 3 2
9 6 5 4 3
8 7 6 5 4
35
LRTA Example
10 START 11 12 1 0 GOAL
11 12 11 2 1
10 11 10 3 2
9 6 5 4 3
8 7 6 5 4
36
LRTA Example
10 START 11 12 1 0 GOAL
11 12 11 2 1
10 11 10 3 2
9 6 5 4 3
8 7 6 5 4
37
LRTA Example
10 START 11 12 1 0 GOAL
11 12 11 2 1
10 11 10 3 2
9 6 5 4 3
8 7 6 5 4
38
LRTA Example
10 START 11 12 1 0 GOAL
11 12 11 2 1
10 11 10 3 2
9 6 5 4 3
8 7 6 5 4
39
LRTA Example, search restarted
10 START 11 12 1 0 GOAL
11 12 11 2 1
10 11 10 3 2
9 6 5 4 3
8 7 6 5 4
40
LRTA Example, search restarted
10 START 13 12 1 0 GOAL
11 12 11 2 1
10 11 10 3 2
9 6 5 4 3
8 7 6 5 4
41
LRTA Example, search restarted
10 START 13 12 1 0 GOAL
11 12 11 2 1
10 11 10 3 2
9 6 5 4 3
8 7 6 5 4
42
LRTA Example, search restarted
10 START 13 12 1 0 GOAL
11 12 11 2 1
10 11 10 3 2
9 6 5 4 3
8 7 6 5 4
43
LRTA Example, search restarted
10 START 13 12 1 0 GOAL
11 12 11 2 1
10 11 10 3 2
9 6 5 4 3
8 7 6 5 4
44
LRTA Example, search restarted
10 START 13 12 1 0 GOAL
11 12 11 2 1
10 11 10 3 2
9 6 5 4 3
8 7 6 5 4
45
LRTA Example, search restarted
10 START 13 12 1 0 GOAL
11 12 11 2 1
10 11 10 3 2
9 6 5 4 3
8 7 6 5 4
46
LRTA Example, search restarted
10 START 13 12 1 0 GOAL
11 12 11 2 1
10 11 10 3 2
9 6 5 4 3
8 7 6 5 4
47
LRTA Example, search restarted
10 START 13 12 1 0 GOAL
11 12 11 2 1
10 11 10 3 2
9 6 5 4 3
8 7 6 5 4
48
LRTA Example, search restarted
10 START 13 12 1 0 GOAL
11 12 11 2 1
10 11 10 3 2
9 6 5 4 3
8 7 6 5 4
49
LRTA Example, search restarted
10 START 13 12 1 0 GOAL
11 12 11 2 1
10 11 10 3 2
9 6 5 4 3
8 7 6 5 4
50
LRTA Example, search restarted
10 START 13 12 1 0 GOAL
11 12 11 2 1
10 11 10 3 2
9 6 5 4 3
8 7 6 5 4
51
LRTA Example, search restarted
10 START 13 12 1 0 GOAL
11 12 11 2 1
10 11 10 3 2
9 6 5 4 3
8 7 6 5 4
52
LRTA Example, search restarted
10 START 13 12 1 0 GOAL
11 12 11 2 1
10 11 10 3 2
9 6 5 4 3
8 7 6 5 4