Soar capabilities
- Basic PSCM functions: State Elaboration, Operator Proposal, Operator Evaluation, Internal Operator Application
- This agent is packaged with the Eaters environment.
- None.
- John Laird
- Soar 8
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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Eaters (Jump)
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Soar
Eaters (Jump)
A very simple eaters agent that will jump in the direction of a cell two steps away in a direction if the location does not contain a wall.
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Eaters (Jump and Move)
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Eaters (Jump and Move)
An eaters agent that both moves and jumps to squares based on the number of points it gains from entering that square minus the number of points it loses for moving (0 for moving, -5 for jump).
Soar capabilities- Basic PSCM functions: State Elaboration, Operator Proposal, Operator Evaluation, Internal Operator Application
- This agent is packaged with the Eaters environment.
- None.
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Eaters (Advanced Move)
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Eaters (Advanced Move)
An eaters agent that implements a generalized move operator that proposes moves to any adjacent position that is empty, has food or contains another eater. It prefers operators that move towards bonus food and avoids operators that move to empy spots or ones with an eater in them. Unlike the basic move agent, this one will avoid repeating the previous move.
Soar capabilities- Basic PSCM functions: State Elaboration, Operator Proposal, Operator Evaluation, Internal Operator Application
- Demonstrates
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Graph Search
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Graph Search
An agent that performs graph search using the selection-astar default rules. This approach can be modified to use any of the different selection approaches.
The basic idea behind this agent is that there is a mission to go from place to place (in the mission structure) using go-to-location for each go-to-location use an iterative form of A* search to find the minimal path between each place iteratively select go-to-waypoint to move through the graph
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Graph Search (with Semantic Memory)
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Graph Search (with Semantic Memory)
This agent demonstrates the use of semantic memory by extending the capabilities of the Graph Search Agent. The description of that agent also applies here.
Has two different uses of semantic memory:- Can hold its mission in wm or smem, which is a list of waypoints to visit. This works fine.
^parameters.mission-storage [ wm smem ] - Can hold all of its waypoints in semantic memory or working memory.
^waypoint-storage [ smem wm ]
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Knowledge Base Agent
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Knowledge Base Agent
This agent has a knowledge base of animal species in working memory from the web. For the first few decisions, it "observes" each entry in the kb (i.e. there is a WME that refers to each of the kb entries, once per decision), storing each to episodic memory. The agent also has a set of unit tests in working memory. Each test is composed of "steps," where a step has properties like "type" (e.g. query vs. retrieve), the "expected" outcome, the "command"... -
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Infinite Mario RL
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Infinite Mario RL
Various agents for learning and playing Infinite Mario domain from RLCompetition 2009. They encode different state representations and learning strategies.
Soar capabilities- Reinforcement learning
- This agent is packaged with the Infinite Mario environment download.
- None.
- None
- Mohan, S. and Laird, J. E. (2011). An Object-Oriented Approach to Reinforcement Learning in an Action Game.
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Missionaries and Cannibals (Planning)
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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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Missionaries and Cannibals (Simple)
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Missionaries and Cannibals (Simple)
This file provides a Soar system to solve the missionaries and cannibals problem using simple search through the space of possible states.
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... -
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TextIO Example
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TextIO Example
A simple agent that computes the sum of two numbers received via SoarTextIO. This agent is used in the SoarTextIO tutorial. SoarTextIO allows you to communicates with an agent via plain sentences, which it translates into a linked list of words.
Soar capabilities- Demonstrates how SoarTextIO can be used.
- The agent is included with the Soar TextIO system under the Agents directory.
- None.
- None.
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