A culturally-situated agent to support intercultural collaboration

While traveling, foreign visitors encounter new products that they need to understand. One solutionis by making Culturally Situated Associations (CSA) i.e. relating the products they encounter to products in their own culture. We propose the design of a system that provides tourists with CSA to help them understand foreign products. In order to provide tourists with CSA that they can understand, we must gather information about their culture, provide them with the CSA, and make sure they understand it. To deliver CSA to foreign visitors, two types of data are needed: data about the products, their associated properties and relationships, and data about the tourist cultural attributes such as country, region, language. The properties and relationships about countries, regions and products, can be extracted from open linked data on the web, and CSA can then be constructed. However, information about the tourist’s cultural attributes and the knowledge they can relate to is unavailable. One way to tackle this problem would be to extract the tourist’s cultural attributes that are needed in each situation through dialogue systems. In this case, a Culturally Situated Dialogue (CSD) must take place. To implement the dialogue, dialogue systems must follow a machine-learned dialogue strategy as previous work has shown that a machine-learned dialogue strategy outperform the handcrafted dialogue approach. We propose the design of a system that uses a reinforcement learning algorithm to learn CSD strategies that can support individual foreign tourists. Since no previous system providing CSA has been implemented, the system allows the creation of CSD strategies when no initial data or prototype exists. The method is used to generate 3 different agents that learn 3 different dialogue strategies.