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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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  • Taxi (Reinforcement Learning)

    Taxi (Reinforcement Learning)

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

    Soar capabilities
    • Reinforcement Learning
    Download Links
    • The agent is included in the Taxi download.
    External EnvironmentDefault Rules
    • None.
    Associated PublicationsDevel...
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  • TankSoar (Obscure Bot)

    TankSoar (Obscure Bot)

    The Obscure-bot is an advanced bot that uses mapping. This is a good bot to test your own bot against. To avoid competitors using its code or reverse engineering it, the productions have been saved in a binary form and many of the original names have been replaced by obscure symbols.

    To use the Obscure-bot, load in the file obscure-bot through the tank-soar control panel. This in turn loads in the binary file obscure-bot.soarx.

    Alternatively, you can create a tank using...
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  • TankSoar (Mapping)

    TankSoar (Mapping)

    This agent extends the capabilities of both the TankSoar simple agent (described here) and the TankSoar simple sound agent (described here) with the ability to create an internal representation of the environment map. It uses this map to better control its radar and find chargers.

    Soar capabilities
    • Hierarchical task composition
    • Creating persistent working memory structures to remember past state
    Download Links
    • This agent is packaged with the TankSoar environment.
    External Envir...
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  • TankSoar (Simple Sound)

    TankSoar (Simple Sound)

    This agent extends the capabilities of the TankSoar simple agent (described here) with the ability to remember hearing where another tank is. The agent can then continue to try to chase the other tank even if it can no longer sense it.

    Soar capabilities
    • Hierarchical task composition
    • Creating persistent working memory structures to remember past state
    Download Links
    • This agent is packaged with the TankSoar environment.
    External EnvironmentDefault Rules
    • None.
    Associated...
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  • TankSoar (Wander)

    TankSoar (Wander)

    This is a very simple agent that wanders the map and adjusts its radar power.

    Wandering consists of moving around the map, using sensors to avoid bumping into obstacles and to detect other objects. To best utilize a tank's radar, which works from the front of the tank this agent prefers to move forward and turn only to avoid obstacles. The radar uses up energy, so it attempts to use it sparingly. The simplest thing to do is to turn on the radar when the tank turns, and turn it off if...
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  • TankSoar (Simple)

    TankSoar (Simple)

    This agent implements a tank that wanders around the board looking for objects. It is also able to chase and attack other agents, as well as retreat.

    The agent uses abstract operators that it decomposes into complex combinations of low-level actions. The tank uses its knowledge to select between these activities based on the current situation, just as it selects between different operators. Both of these problems are handled in Soar by allowing abstract high-level operators to be implemented...
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  • Blocks-World (Reinforcement Learning)

    Blocks-World (Reinforcement Learning)

    This agent contains a version of blocks-world that uses reinforcement learning for move-block, which moves a block to a destination.

    Soar capabilities
    • Reinforcement Learning
    Download LinksExternal Environment
    • None.
    Default Rules
    • None.
    Associated PublicationsDeveloper
    • John Laird
    Soar Versions
    • Soar 8,9
    Project Type
    • VisualSoar
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  • Blocks-World (Hierarchical Look-Ahead)

    Blocks-World (Hierarchical Look-Ahead)

    This project augments the Blocks-World Hierarchical Agent with look-ahead state evaluation. The description of the original agent applies to this one. The main difference is that look-ahead is performed in the middle of the three problem spaces that it uses.

    Soar capabilities
    • Hierarchical task composition via subgoaling
    • Look-ahead subgoaling
    • Internally simulates external environment including an i/o link
    Download LinksExternal Env...
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  • Blocks-World (Hierarchical)

    Blocks-World (Hierarchical)

    This project contains a version of blocks world that is formulated for hierarchical task decomposition. It involves three levels of problem spaces. There is sufficient evaluation knowledge so that there is no search/uncertainty at every level. The top level has a single operator: move-block, which moves a block (moving-block) to a destination. The destination can be the top of another block or the table.

    The next level consists of two operators: pick-up and put-down and they arise in...
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  • Blocks-World (Subgoaling with RL)

    Blocks-World (Subgoaling with RL)

    agent incorporates both operator subgoaling/means ends analysis with reinforcement learning. All search control knowledge (operator evaluation rules) are removed from blocks-world-operator-subgoaling and instead there are RL rules supplemented with rules to compute reward, both in the top state and the substate. Implemented for four blocks.

    Soar capabilities
    • Subgoaling with means-ends analysis
    • Reinforcement learning
    Download LinksExternal En...
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