Otago New Zealand Distributed Information Systems Research Group meeting of 4th June 2020

1. AI for Multi-Agent
Programming Contest 2020
Cleber Jorge Amaral
Supervised by Professor Stephen Cranefield and Jomi Fred Hubner
nzdis meeting of 4th June 2020

2. This is not a seminar, please, join in this discussion!
● Multi-Agent Contest is a challenge where teams (of developers)
are encouraged to develop teams (or agents)
● The annual contest aims improve testing multi-agent
programming (MAOP) languages, platforms and tools, and
identify their weak and strong aspects in MAOP by:
○ identifying key problems,
○ collecting suitable benchmarks, and
○ gathering test cases which require and enforce coordinated action

3. This year competition
● The teams that are enrolled have to develop their agents using
a given simulation package
● Each team must provide a mean for the agents to connect to
the server for further matches
● On 24th August 2020 the teams will be qualified to have sure
they can participate on the matches
● The organisation of the contest will define an agenda for
matches between teams which will occur during
September/October 2020

4. This year challenge: Agents Assemble II

5. Agents Assemble II: Introduction
● Two teams of agents moving on a grid.
● The goal is to explore the world and acquire blocks to
assemble them into patterns.
● Agents can attach things to their 4 sides. The attached things
move or rotate with them.
● Two agents can connect things that are attached to them.

6. Agents Assemble II: Environment
● The environment is a rectangular grid with unknown dimension.
● The grid loops horizontally and vertically.
● Each cell contains up to one thing, that can be:
○ Entities: can move around and attach themselves to things.
○ Blocks: Each block has a specific type.
○ Dispenser: can be used to retrieve a specific kind of block.
○ Marker: A marker marks a cell, used for the clear.
● Terrains:
○ empty: If nothing else is specified, a cell is just a cell.
○ goal: Agents have to be on a goal cell to submit a task.
○ obstacle: block movement and rotations.

7. Agents Assemble II: Agents
● Agents do not know their absolute positioning in the
environment.
● Agents only perceive positions relative to their own.
● They only know their energy level (used for clearing) and
whether they are currently disabled. Agents automatically
recharge 1 energy per step.
● If an agent becomes disabled, it loses all of its attachments and
remains inactive for a fixed configurable number of steps.

8. Agents Assemble II: Tasks
● Tasks have to be completed to get score points.
● They appear randomly during the course of the simulation.
○ name
○ deadline: the last step to submit
○ reward
○ requirements: the block to be attached
■ x/y: the agent being (0,0)
■ type
● An agent can accept a task if it is near a task board. Each
agent can only hold one task at a time. Only an agent, who has
accepted the task before, can submit it.

9. Agents Assemble II: Actions
● In each step, an agent may execute exactly one action.
○ Skip
○ Move
○ Attach
○ Detach
○ Rotate
○ Connect (two agents connect things attached to them)
○ Disconnect
○ Request (a new block from a dispenser)
○ Submit
○ Clear
○ Accept

10. Agents Assemble II: Perception
● In each step, each agent perceives:
○ Score
○ lastAction, lastActionResult, lastActionParams:
○ Energy
○ Disabled
○ Task (accepted task)
○ Things (visible to the agent)
○ Clear (the cell is about to be cleared)
○ Terrain
○ Task (currently active), name, deadline, reward...
○ attached

11. Agents Assemble II: Final remarks
● The environment randomly generates clear events.
● An agent is attached to another, can move twice as fast!
● Agents must know how to “dig” themselves out of obstacles.
● Clear actions could also be used to scare away opponent
agents.

12. Discussion: Some opportunities to apply AI
● Exploration
● Find and accept tasks
● Assemble structures
● Deliver structures
● Move fast
● Defend the team
● Attack the opponent
● Deal with uncertainties
● Form teams
● Decide the best thing to do
● Use energy wisely
● Last year experience: only automated planning was used (which took longer
than turns interval - 4 seconds).

13. Thanks!
More information:
https://multiagentcontest.org/2020/