Evolving under tasks of incomplete information: streaming and self play

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Tasks that only are only partially observable present unique challenges for machine learning in general and genetic programming (GP) in particular. The notion of a partially observable application will be considered from two specific application contexts of relevance to the partner organization. The first context is that of streaming data analysis under limited label availability. The goal is to begin with some small subset of labelled data representing the current status of a task, and then be able to recognize when the underlying nature of the non-stationary process generating the data stream has changed. On recognizing such a change in the stream's behaviour, the current GP solutions should selectively request labels for the data corresponding to the change. Decisions then need to be made to determine which GP individuals currently employed for detecting change / labelling the stream, should be updated or replaced. The second application context is that of learning to construct non-player character (NPC) through self play under a game of incomplete information. A NPC is computer based entity who appears in a game to provide additional interest for human players. In this work we are particularly interested in the case of NPC who appear in the game of poker. The goal however, is not to produce the strongest possible player, but to provide players who are complementary to the human players currently participating. Poker represents an interesting task domain because it is also based on incomplete information (no knowledge of the other player's cards), stochastic (cannot predict the order of card apparence) and the capability of opponent players is unknown. The stochastic nature of the task, lack of complete information and non-stationary nature of strategies adopted by other players all make the poker task particularly challenging from the perspective of machine learning.