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.
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.
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...
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...
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')
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.
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...