Intelligent tutoring agent for settlers of Catan

An Intelligent Tutoring Agent (ITA) for the board game Settlers of Catan (SoC) is introduced. It uses CLIPS knowledge bases, connected by JCLIPS to a JAVA implementation of SoC. It is founded on a new theoretical framework that describes the development of negotiation skills in children. Using this framework, the ITA helps children in developing negotiation skills through play, which makes it unique in its kind

Agent Technologie: Online leren onderhandelen

Het onderzoek naar spellen is een belangrijk deelgebied binnen AI. Dit artikel beschrijft onderzoek naar het bordspel Kolonisten van Catan. Dit spel herbergt meerdere facetten die interessant zijn vanuit een AI-perspectief. Zoals bij alle spellen is er de vraag hoe winnende strategieën te ontwikkelen. Dit probleem kan zowel vanuit een technisch AI-perspectief als vanuit een cognitieperspectief worden benaderd. Zo kan bijvoorbeeld een agent worden ontwikkeld die een menselijke speler kan vervangen. Dit artikel beschrijft onderzoek dat is gedaan vanuit het cognitieperspectief, daar het doel was inzicht te krijgen in de regels achter de ontwikkeling van menselijke onderhandelingstechnieken

E-Learning through gaming: unfolding children’s negotiation skills

A generic theoretical framework on teaching children to negotiate is presented, founded on Piaget’s child development and Thompson and Hastie’s negotiation theories and validated through an experiment. The framework was implemented as CLIPS knowledge base, the back-end of an Intelligent Tutoring Agent (ITA). Negotiation skills were assessed through an online JAVA implementation of the board game Settlers of Catan (SoC). The CLIPS knowledge base was connected by JCLIPS to SoC. The ITA was thoroughly tested and found to be robust, with an excellent multithread handling. After installing a client, SoC can be played over Internet against other artificial or/and human players. The integrated ITA helps children to improve their negotiation skills and helps science to improve the theoretical framework, which makes it unique in its kind

Number of associations.

<p>Number of associations for prime words in the test blocks.</p

Decoding results.

<p>First two columns indicate proportion correct, last column indicates the number of probes used to obtain the accuracy for the <i>stop</i> condition, for the <i>full condition</i> this is always 100. Asterisks indicate whether the proportion correct differs significantly from chance level (1/150, 0.00667). * indicates .001 < p <.05, ** indicates p <.001.</p

Association strength.

<p>Average association strength per block. The error bars indicate the standard deviation.</p

Post-hoc simulations.

<p>Results for post-hoc simulations: Exp Full: the experiment using the full number of probes, Exp: results from the experiment with early stopping, Sim: simulation results with early stopping, Rand Sim: simulation with random probe selection and early stopping, Sim 150×10k: simulation with 150 targets and 10.000 probes with early stopping, Sim 10k×10k: simulation with 10.000 targets and 10.000 probes with early stopping. Top-left: Proportion correct, related correct and in top 10. Top-right: Rank, the rank for the last analysis (Sim 10k×10k) is scaled by dividing by 61.8. Bottom-left: Number of probes. Bottom-right: Information Transfer Rate. * indicates .001 < p <.05, ** indicates p <.001.</p

Classification accuracies.

<p>Single trial classification accuracies, based on relatedness labels from the Leuven dataset. All classification accuracies differ significantly from chance level (0.5) with a p-value of <.001.</p