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Multi-Agent Programming Contest

Multi-Agent Programming Contest is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Multi-Agent Programming Contest rather than just read about it. In short: The Multi-Agent Programming Contest is an annual international programming competition with stated goal of stimulating research in the area of multi-agent system development and programming. History In 2005, Jürgen Dix (Clausthal University of Technology), Mehdi Dastani (University Utrecht) and Peter Novák (Czech Technical University in Prague) have brought the contest into being and running.

Key takeaways

  • Multi-Agent Programming Contest belongs to computer science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Multi-Agent Programming Contest to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Multi-Agent Programming Contest from memory before moving on to harder problems.

Reference excerpt

The Multi-Agent Programming Contest is an annual international programming competition with stated goal of stimulating research in the area of multi-agent system development and programming.

History In 2005, Jürgen Dix (Clausthal University of Technology), Mehdi Dastani (University Utrecht) and Peter Novák (Czech Technical University in Prague) have brought the contest into being and running. The competition originally focused on Logic programming of Multi-agent systems. The goals, raised in 2005, have proven to be a solid basis for multi-agent system development and are still valid:

Identification of key problems To collect suitable benchmarks. In 2007, a third goal has been added:

To gather test cases which require and enforce coordinated action. Although it is necessary to find a solution for the contest quest to win, the organizers pursue the intention that the solution is a system of cooperating autonomous programs that achieve the objectives together. They are also interested in how the contest participants develop the solution.

Scenarios

Food collectors Agents have to look for food and bring it to a depot on a two-dimensional grid world. Each cell can contain an agent, or food. The agents can only see a small part of the map. Initially there is no food available, it appears randomly during the game, so that agents need to search the map constantly in order to win. This scenario was used in 2005.

Gold miners On a grid based map, teams of agents look for gold and transport it to the depot. As opposed to the food scenario, cells can also contain trees which block the agents and can form more or less complex labyrinths. Also, there are now two opposing teams competing for the gold. This scenario was used in the contests of 2006 and 2007. In 2007, the scenario was extended to allow the agents to carry more than one piece of gold, and to push opposing agents aside.

Cowboys A grid based map contains trees, corrals, cows and agents. Two opposing teams try to drive as many cows as possible in ones corral. Cows behave using Swarm intelligence. They are also afraid of cowboys and try and run away. This scenario was used in 2008, 2009 and 2010. For the last two years, gates were introduced to make the scenario more challenging.

Agents on Mars The 2011 contest introduces a scenario called agents on mars. Goal is to conquer as much space on mars as possible, using a team of cooperating agents. The challenge here is the higher complexity resulting from the introduction of five roles with different properties and abilities, which have to be used to scout, conquer, and keep the conquered land. The team HactarV2 from the TU-Delft won the 2011 competition while using the GOAL programming language.

References Toni, Francesca; Torroni, Paolo (Eds.): Computational Logic in Multi-Agent Systems. 6th International Workshop, CLIMA VI 2005. Springer 2006 - ISBN 978-3-540-33996-0. Dastani, M.; El Fallah Seghrouchni, A.; Ricci, A.; Winikoff, M. (Eds.): Programming Multi-Agent Systems. Fifth International Workshop, ProMAS 2007. Springer 2008 - ISBN 978-3-540-79042-6 Tristan Behrens, Mehdi Dastani, Jürgen Dix, Michael Köster, Peter Novak: Multi-Agent Contest 2009 Edition Multi-agent Programming Contest 2013: The Teams and the Design of their Systems. In: M. Cossentino, A. El Fallah Seghrouchni, M. Winikoff (Eds.): Engineering Multi-Agent Systems. 1st Intl. Workshop. EMAS 2013. Revised Selected Papers. Springer 2013 - ISBN 978-3-642-45343-4

External links Multi-Agent Programming Contest

Worked examples

Example 1 — a first encounter with Multi-Agent Programming Contest

Start with the simplest possible case. Write down what Multi-Agent Programming Contest claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Multi-Agent Programming Contest before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Multi-Agent Programming Contest ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Multi-Agent Programming Contest

In research
Multi-Agent Programming Contest appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Multi-Agent Programming Contest in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Multi-Agent Programming Contest is common in secondary-school and first-year university syllabi. It links to neighbouring topics Agent-based model, Information science, Logic in computer science, so understanding it makes those chapters shorter.
In everyday life
Look for Multi-Agent Programming Contest outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Multi-Agent Programming Contest in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Multi-Agent Programming Contest means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Multi-Agent Programming Contest out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Multi-Agent Programming Contest in simple terms?

The Multi-Agent Programming Contest is an annual international programming competition with stated goal of stimulating research in the area of multi-agent system development and programming. History In 2005, Jürgen Dix (Clausthal University of Technology), Mehdi Dastani (University Utrecht) and Pet…

Why does Multi-Agent Programming Contest matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Multi-Agent Programming Contest?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Multi-Agent Programming Contest.

Tags

  • Agent-based model
  • Information science
  • Logic in computer science
  • Programming contests

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