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AgentCentered Search

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Title: AgentCentered Search


1
Agent-Centered Search
  • Mitja Lutrek
  • Department of Intelligent Systems,
  • Joef Stefan Institute

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
7
A Example
8
A Example
9
A Example
10
A Example
11
A Example
12
A Example
13
A Example
14
A Example
15
A Example
16
A Example
17
A Example
18
A Example
19
A Example
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),
  • it is not known in which part of the space the
    game will head (environment not wholly known).
  • This is an example of two-agent search, I 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
24
LRTA Example
25
LRTA Example
26
LRTA Example
27
LRTA Example
28
LRTA Example
29
LRTA Example
30
LRTA Example
31
LRTA Example
32
LRTA Example
33
LRTA Example
34
LRTA Example
35
LRTA Example
36
LRTA Example
37
LRTA Example
38
LRTA Example
39
LRTA Example, search restarted
40
LRTA Example, search restarted
41
LRTA Example, search restarted
42
LRTA Example, search restarted
43
LRTA Example, search restarted
44
LRTA Extensions
  • Unknown environment, agents sensory range very
    limited
  • minimal local search space (only the agents
    position).
  • Unknown environment, the task is exploration
  • maximal local search space (all known positions),
    agent moves towards the closest unvisited
    position
  • node counting agent moves towards the least
    frequently visited adjacent position.
  • Known environment, unknown starting position
  • minimize the worst-case execution time
  • min-max LRTA
  • a minimax tree is built around the agents
    position
  • the agents actions minimize the length of the
    path to the goal
  • possible configurations of the environment
    maximize the length of the path to the goal.

45
Search Pathology
  • Minimax Nau, 1979 Beal, 1980 Bratko Gams,
    1982 etc.
  • in practice, the more moves ahead one searches,
    the better he plays
  • in theory, under apparently reasonable
    conditions, the more moves ahead one searches,
    the worse he plays
  • this is caused by minimax amplifying the error of
    the heuristic evaluation used in the leaves of
    the game tree.
  • Agent-centered search Bulitko et al., 2003
  • one would expect that the larger the local search
    space, the more likely an agent is to choose the
    optimal path
  • in some cases, the larger the local search space,
    the less likely an agent is to choose the optimal
    path.
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