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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 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 (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 subgoaling
    • Can
    ...
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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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  • 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
    • Can learn procedural knowledge (enable with 'learn always')
    Download Li...
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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)

    Blocks-World (Subgoaling)

    This agent is a modified version of the simple blocks-world agent that uses means-ends analysis and operator subgoaling (first used in General Problem Solver (GPS)).

    Means-ends analysis involves proposing operators that can achieve part of the goal. Thus, some operators will be proposed even if they do not apply to the current state. If an operator is selected that can not apply, an operator no-change impasse arises. In that substate, the goal is to achieve a state in which the impassed...
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  • Blocks-World (Look-Ahead with RL)

    Blocks-World (Look-Ahead with RL)

    This agent contains a version of blocks-world that involves one level of problem spaces and look-ahead but with two important extensions. It demonstrates how RL-rules can be learned by chunking and then updated in the future. The advantage over simple lookahead is that it doesn't lock on to the one path found during look-ahead after chunking. It will still do some exploration.

    Soar capabilities
    • Reinforcement Learning
    • Chunking
    • Look-ahead Subgoaling
    • Can learn procedural knowledge
    ...
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  • Blocks-World (Look-Ahead)

    Blocks-World (Look-Ahead)

    This agent contains a version of blocks-world that involves one level of problem spaces and look-ahead. It is based on the simple blocks-world agent.

    Soar capabilities
    • Look-ahead subgoaling
    • Can learn procedural knowledge (enable with 'learn always')
    Download LinksExternal Environment
    • None.
    Default Rules
    • simple.soar
    • selection.soar
    Associated PublicationsDeveloper
    • John Laird
    Soar Versions
    • Soar 8,
    ...
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  • Counting Agent

    Counting Agent

    Agents that can count in nine different ways.

    The real purpose of this agent is to provide various test of Soar's execution:
    1. Long runs
    2. Lots of working memory
    3. Various subgoals/impasses and results with chunking
    4. Lots of chunks/results
    5. Nasty conditions for chunking
    6. Should find any memory leaks.

    Soar capabilities
    • Subgoaling
    • Chunking
    Download Links
    External Environment
    • None.
    Default Rules
    • None.
    Associated Publications
    • None.
    Developer
    • John
    ...
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  • Missionaries and Cannibals (Planning)

    Missionaries and Cannibals (Planning)

    This file provides a Soar system to solve the missionaries and cannibals problem using look-ahead planning.

    There are three missionaries and three cannibals on one side of a river. There is a boat on their bank of the river that can be used by either one or two persons at a time. This boat must be used to cross the river in such a way that cannibals never outnumber missionaries on either bank of the river.

    Simple state representation where the state has two objects: one...
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  • Arithmetic

    Arithmetic

    An agent that performs multi-column addition and subtraction with borrowing and carrying, all the way down to counting. No math functions are used.

    This program supports arithmetic ands subtraction between two multi-digit numbers. It formulates the problem in multiple columns. It does not use any math functions. As currently formulated, it uses a table of all single digit addition facts (for addition and one subtraction strategy) and tables of simple subtraction facts and addition by...
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  • Algebra Solver

    Algebra Solver

    This is an agent that can solve simple algebra problems with one variable. It parses raw text input from Soar Text I/O into a tree structure, solves the problem in that representation and then prints out the answer.

    Soar capabilities
    • Hierarchical task decomposition
    • Chunking
    Download LinksExternal EnvironmentDefault Rules
    • None.
    Associated Publications
    • None.
    Developer
    • John Laird
    Soar Versions
    • Soar 8,9
    Project Type
    • VisualSoar
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