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Agent-Centered Search

Agent-Centered Search. Mitja Luštrek Department of Intelligent Systems, Jožef Stefan Institute. 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.

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Agent-Centered Search

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  1. Agent-Centered Search Mitja Luštrek Department of Intelligent Systems, Jožef 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 agent’s 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.

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  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 space:h (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.

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  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, agent’s sensory range very limited: • minimal local search space (only the agent’s 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. • Unknown starting position: • minimize the worst-case execution time; • min-max LRTA*: • a minimax tree is built around the agent’s position; • the agent’s 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 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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