All Classes and Interfaces
Class
Description
Measures Jadex performance by executing several configure goals.
A block in the blocks-world.
Cell renderer for blocks.
A list model representing a collection of blocks
on a table or in a bucket.
Blocksworld agent for stacking blocks.
Shows the gui for blocksworld.
Shows the blocksworld.
The board containing places, pieces and played moves.
The control part of
The board gui.
Display the board.
(Knowledge about) a charging station.
Meta-level reasoning plan for choosing between applicable plans.
Cleaner object represents knowledge about a cleaner robot.
BDI agent template.
First BDI agent with a goal and a plan.
Use the recur flag to execute goals periodically.
Use many plans for the same goal.
Use goal settings to control plan selection.
Use a belief to control a declarative goal.
Using deliberation settings for managing conflicting goals.
Separate maintain and target conditions.
Managing known charging stations in a belief set.
A subgoal for knowing charging stations
A Plan for Finding a Charging Station
A cleanup goal for each piece of waste.
Separate Maintain and Target Conditions.
Separate Maintain and Target Conditions.
Separate Maintain and Target Conditions.
Using inner classes for plans with conditions.
More or less working solution for a BDI cleaner.
Clear a block.
Stack blocks according to the target configuration.
The environment object for non distributed applications.
The gui for the cleaner world example.
Simple hello agent that activates a plan based on a belief change.
BDI agent that uses belief to trigger goal and execute plans.
The interface for the playing board.
(Knowledge about) a charging station.
Cleaner object represents knowledge about a cleaner robot.
A location on the virtual map.
Base interface for all environment opbjects.
Environment representation of a pheromone.
(Knowledge about) a piece of waste.
(Knowledge about) a waste bin.
The View Board represents the puzzle board and the pegs.
Editable Java class for concept Location of cleaner-generated ontology.
Base class for all map objects.
Main for starting the example programmatically.
Main for starting the example programmatically.
Main for starting the example programmatically.
Main class for starting a cleaner-world scenario
Main for starting the example programmatically.
A move consisting of a start and an end point.
Sort moves according to a strategy.
Make a move and dispatch a subgoal for the next.
Environment representation of a pheromone.
A piece for playing.
A position has two coordinates.
The sensor / actuator gives access to the perceived environment
and provides operations to manipulate the environment.
The GUI for the cleaner world example.
Simple cleaner with a main loop for moving randomly.
Agent without POJO class, just lambda body.
Possible solution for exercise zero (non-BDI cleaner).
Simple example of using the environment sensor.
Puzzling agent.
Puzzle agent tries to solve a solitair board game
by recursiveky applying means-end-reasoning.
Stack a block on top of another.
A table in the blocks-world.
Go to university example taken from
Winikoff, Padgham: developing intelligent agent systems, 2004.
The take x goal is for using a train or tram.
(Knowledge about) a piece of waste.
(Knowledge about) a waste bin.