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Agents

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In this category, you'll find a variety of agents developed for a wide range of different tasks and environments. You will find a full description of the agent, its capabilities and problem solving approach as well, as a download link. If the agent requires an environment, a link will be provided. We'd highly encourage you to submit your own agents for use by the greater Soar community. To do so, you can send your zipped up submission with a full description (try to include all of the type of information we include on each download page) to mazina@umich.edu with the subject "Soar Agent Submission".

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  • Water Jug (Reinforcement Learning)

    Water Jug (Reinforcement Learning)

    This agent demonstrates how to solve the water jug problem using reinforcement learning. It is a modification of the Simple Water Jug Agent. Two main changes are made: templates are used to generate the full space of possible moves that the agent can perform and rewards are set based on the problem solution.

    Soar capabilities
    • Reinforcement learning
    Download LinksExternal Environment
    • None.
    Default Rules
    • None.
    Associated Publications
    • The
    ...
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  • Water Jug (Look-Ahead with State Evaluation)

    Water Jug (Look-Ahead with State Evaluation)

    This agent is a modification of the Water Jug Simple Agent that demonstrates using a tie impasse to subgoal and evaluate operators. This is an excellent demonstration of look-ahead search and how the selection default rules work.

    Unlike the Water Jug Look-Ahead Agent, the tie agent has knowledge that prefers certain moves over others, for example preferring a pour after a fill operator, so it is able to solve the problem much more efficiently.

    Soar capabilities
    • Look-ahead
    ...
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  • Water Jug (Hierarchical Task Decomposition)

    Water Jug (Hierarchical Task Decomposition)

    This project contains a version of the water jug problem that is formulated for hierarchical task decomposition. It involves two levels of problem spaces. The top level has three operators: fill, empty or pour. The next level consists of three operators: pick-up, fill-jug and put-down and they arise in an operator no-change for the super state.

    Note: This agent works with chunking, which compiles the actions in the substates into rules that apply at the top-state. Use the command "learn...
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  • Water Jug (Look-Ahead)

    Water Jug (Look-Ahead)

    This agent is a modification of the Water Jug Simple Agent that demonstrates using a tie impasse to subgoal and evaluate operators. This is an excellent demonstration of look-ahead search and how the selection default rules work.

    Unlike the Water Jug Look-Ahead Agent with State Evaluation , the look-ahead agent has no knowledge that prefers certain moves over others, so it does exhaustive search which takes far more decision cycles.

    Soar capabilities
    • Look-ahead subgo
    ...
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  • Water Jug (Simple)

    Water Jug (Simple)

    These Soar productions implement the water-jug task. The task is to find the sequence of steps that fill the three gallon jug with one gallon of water. There are a well that has an infinite amount of water, a five gallon jug, and a three gallon jug.

    The task problem space has three operators: empty, fill, and pour. Empty empties a jug into the well. Fill fills up a jug from the well. Pour pours some or all of the contents from one jug into the other jug. Pour can only pour out the contents...
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  • Tower of Hanoi (Simple)

    Tower of Hanoi (Simple)

    This agent solves the Tower of Hanoi problems. This puzzle "involves three vertical pegs or posts and a number of doughnut-like disks of graduated sizes that fit on the pegs. At the outset, all the disks are arranged pyramidally on one of the pegs, say A, with the largest disk on the bottom. The task is to move all of the disks to another peg, C, say, under the constraints that (1) only one disk may be moved at a time, and (2) a disk may never be placed on top of another smaller than itself....
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  • Tower of Hanoi (Recursive)

    Tower of Hanoi (Recursive)

    This agent solves the problem using a recursive strategy. It tries to always moves the biggest out of place disk into its correct position. The general description of the task from the Simple Tower of Hanoi Agent still applies.

    Soar capabilities
    • Basic PSCM functions: State Elaboration, Operator Proposal, Operator Evaluation, Internal Operator Application
    • Recursive problem solving
    Download LinksExternal Environment
    • None.
    Default Rules
    • None.
    Associated...
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  • 15-puzzle

    15-puzzle

    This agent is a straightforward implementation of the fifteen-puzzle. It uses look-ahead search to solve the puzzle with a simple evaluation function. This agent also demonstrates chunking.

    The puzzle consists of fifteen sliding tiles, numbered by digits from 1 to 15 arranged in a 4 by 4 array of sixteen cells. One of the cells is always empty, and any adjacent tile can be moved into the empty cell. The initial state is some arbitrary arrangement of the tiles. The goal state is the...
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  • 8-Puzzle

    8-Puzzle

    This agent is a straightforward implementation of an eight-puzzle. It uses look-ahead search to solve the puzzle with a simple evaluation function. This agent also demonstrates chunking.

    The puzzle consists of eight sliding tiles, numbered by digits from 1 to 8 arranged in a 3 by 3 array of nine cells. One of the cells is always empty, and any adjacent tile can be moved into the empty cell. The initial state is some arbitrary arrangement of the tiles. The goal state is the arrangement...
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  • Taxi (Hierarchical Reinforcement Learning)

    Taxi (Hierarchical Reinforcement Learning)

    This agent simulates an omniscient taxi driver that uses reinforcement learning and hierarchical task decomposition to improve its performance over runs.

    Soar capabilities
    • Hierarchical task decomposition
    • Reinforcement learning
    Download Links
    • The agent is included in the Taxi download.
    External EnvironmentDefault Rules
    • None.
    Associated Publications...
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