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This category contains an extensive list of domains you can develop agents in. Some are game-like environment simulators while other provide access to an external knowledge source for your agent to process and manipulate, for example WordNet or SoarQnA. All of these domains are fully interfaced with Soar already.

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  • WordNet WSD

    WordNet WSD

    This project is a word sense disambiguation task that involves some preliminary work importing a WordNet database into Soar's Semantic Memory. It contains a set of PhP scripts that does various conversions to a format that Soar can use and an agent that uses that knowledge to disambiguate words in various sentences.

    Download LinksAssociated Agents
    • Pending
    • Pending
    Data Source

    We are using data from the WN-LEXICAL project:...
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  • TankSoar


    TankSoar is a multi-agent tank battle game implemented using Java and interfaced with Soar via SML.

    The TankSoar world consists of a rectangular grid, 14 squares wide by 14 squares high. All four sides are bounded by walls made of rock. Interior walls are made of trees. There are a variety of maps that can be used, with different layouts of walls. Each TankSoar agent controls one tank. The tank takes up one square in the playing field. A tank has actions it can take, resources it carries,...
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  • Infinite Mario RL

    Infinite Mario RL

    This environment is derived from the Infinite Mario domain from RLCompetition2009 (based on RL-Glue) and uses SML to connect to Soar.

    Agents play a variant of Super Mario, a complete side-scrolling video game with destructible blocks, enemies, fireballs, coins, chasms, platforms, etc. The state space is complicated, but factored in an object-oriented way, which captures many aspects of the real world. Challenges include:
    • Path planning: How can Mario navigate around simple obstacles,
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  • Eaters


    Eaters is a Pac-Man like game implemented using Java and interfaced with Soar via SML.

    In Eaters, PACMAN-like eaters compete to consume food in a simple grid world. The Eaters world consists of a rectangular grid, 15 squares wide by 15 squares high. Walls bound all four sides. Interior wall sections are randomly generated for each new game. No two walls will touch, so there are no corners, except for exterior walls, and no dead-ends anywhere on the board. Each eater starts at a random...
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  • SoarText/IO


    The purpose of this tool is to allow the Soar programmer to interact with a Soar agent while it is running. SoarTextIO allows you to communicates with an agent via plain sentences that it translates into a linked list of words.

    Why is this useful? Imagine you have written a Soar agent that solves algebra problems. SoarTextIO provides a way for you to give the agent equations to solve on the fly. Or, say that you have written an exploration bot in a simulation whose goal is to find different...
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  • RoomsWorld


    RoomsWorld is a simulated Soar Robot environment. It is composed of a set of connected rooms, some of which contain blocks. One of the rooms is designated the storage room. The agent's task is to collect the blocks and move them to the storage room. The agent can turn and move forward, and pick up and put down a block. The agent can only carry one block at a time. The agent's movement is continuous and takes time (it turns and moves at a fixed rate). The agent's perception is limited by a vision...
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  • Soar QnA

    Soar QnA

    SoarQnA facilitates agent access to external knowledge stores via the io system. It is an efficient, unified agent interface that allows your system to access arbitrary external data sources.

    Environment Properties
    • N/A
    Download LinksAssociated Agents
    • QnA Test Agent: An example agent is provided with the download which runs and validates a non-comprehensive set of unit tests based upon available sample data source instances/queries.

    All of these...
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  • Dice


    Dice is a java implementation of a game often called Liar's Dice.

    Here is an abbreviated description of the rules used in our implementation of this domain:
    • Each player gets five six-sided dice and a cup to conceal their dice from other players.
    • To begin each round, all players roll their dice under their cups and look at their new 'hand' while keeping it concealed from the other players. The first player begins bidding, picking a quantity of a face number. The quantity states the
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  • WordNet WSD (with Parse Trees)

    WordNet WSD (with Parse Trees)

    This project is a word sense disambiguation task that uses a different approach than WordNet for Soar. While they both use the same corpus, this formulation gives the agent a syntactic parse tree (not a graph) and a word, and the agent is asked to disambiguate the word. Many structures are repeated within the tree over multiple sentences.

    Download LinksNote: The domain requires the soar_exp package above (but will soon be rewritten it so it doesn't). Also...
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  • Taxi


    The taxicab problem domain is well known in the area of reinforcement learning. Simply put, a taxicab driver is tasked with the problem of picking up a passenger and delivering him to his destination in as few steps as possible. Typically, the taxi is constrained by a limit on the amount of fuel that can be carried.

    The canonical taxicab problem is a 5x5 gridworld. There are four cells which serve as possible starting locations and possible destinations for the passenger. There is a...
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