Learning explanatory rules from noisy data

Suppose you are playing football. The ball arrives at your feet, and you decide to pass it to the unmarked striker. What seems like one simple action requires two different kinds of thought.First, you recognise that there is a football at your feet. This recognition requires intuitive perceptual thinking -you cannot easily articulate how you come to know that there is a ball at your feet, you just see that it is there. Second, you decide to pass the ball to a particular striker. This decision requires conceptual thinking. Your decision is tied to a justification – the reason you passed the ball to the striker is because she was unmarked.The distinction is interesting to us because these two types of thinking correspond to two different approaches to machine learning: deep learning and symbolic program synthesis. Deep learning concentrates on intuitive perceptual thinking whereas symbolic program synthesis focuses on conceptual, rule-based thinking. Each system has different merits – deep learning systems are robust to noisy data but are difficult to interpret and require large amounts of data to train, whereas symbolic systems are much easier to interpret and require less training data but struggle with noisy data.Read More

Open-sourcing Psychlab

Consider the simple task of going shopping for your groceries. If you fail to pick-up an item that is on your list, what does it tell us about the functioning of your brain? It might indicate that you have difficulty shifting your attention from object to object while searching for the item on your list. It might indicate a difficulty with remembering the grocery list. Or it could it be something to do with executing both skills simultaneously.Read More

Game-theory insights into asymmetric multi-agent games

As AI systems start to play an increasing role in the real world it is important to understand how different systems will interact with one another.In our latest paper, published in the journal Scientific Reports, we use a branch of game theory to shed light on this problem. In particular, we examine how two intelligent systems behave and respond in a particular type of situation known as an asymmetric game, which include Leduc poker and various board games such as Scotland Yard. Asymmetric games also naturally model certain real-world scenarios such as automated auctions where buyers and sellers operate with different motivations. Our results give us new insights into these situations and reveal a surprisingly simple way to analyse them. While our interest is in how this theory applies to the interaction of multiple AI systems, we believe the results could also be of use in economics, evolutionary biology and empirical game theory among others.Read More