Learning how to adjust to an opponent's position is critical to the success of having intelligent agents collaborating towards the achievement of specific tasks in unfriendly environments. This pa(cid:173) per describes our work on a Memory-based technique for to choose an action based on a continuous-valued state attribute indicating the position of an opponent. We investigate the question of how an agent performs in nondeterministic variations of the training situ(cid:173) ations. Our experiments indicate that when the random variations fall within some bound of the initial training, the agent performs better with some initial training rather than from a tabula-rasa.

When I attend customer engagement and industry events, I inevitably field lots of questions that are close to the heart of a data scientist. Many executives are confused by the concepts of machine learning, deep learning, memory-based learning, and artificial intelligence. They wonder about the differences in these technologies, how everything fits together, and what they need to pay attention to. They wonder whether they need all of it or just some of it, and what they need to do to get started. And, yes, I hear people ask whether the ultimate goal is to replace humans with computers.

This paper investigates learning in a lifelong context. Lifelong learning addresses situations in which a learner faces a whole stream of learning tasks. Such scenarios provide the opportunity to transfer knowledge across multiple learning tasks, in order to generalize more accurately from less training data. In this paper, several different approaches to lifelong learning are described, and applied in an object recognition domain. It is shown that across the board, lifelong learning approaches generalize consistently more accurately from less training data, by their ability to transfer knowledge across learning tasks.

Our research works towards this broad goal from a Machine Learning perspective. We are particularly interested in investigating how an intelligent agent can choose an action in an adversarial environment. We assume that the agent has a specific goal to achieve. We conduct this investigation in a framework where teams of agents compete in a game of robotic soccer. The real system of model cars remotely controlled from off-board computers is under development.

This paper investigates learning in a lifelong context. Lifelong learning addresses situations in which a learner faces a whole stream of learning tasks. Such scenarios provide the opportunity to transfer knowledge across multiple learning tasks, in order to generalize more accurately from less training data. In this paper, several different approaches to lifelong learning are described, and applied in an object recognition domain. It is shown that across the board, lifelong learning approaches generalize consistently more accurately from less training data, by their ability to transfer knowledge across learning tasks.

Our research works towards this broad goal from a Machine Learning perspective. We are particularly interested in investigating how an intelligent agent can choose an action in an adversarial environment. We assume that the agent has a specific goal to achieve. We conduct this investigation in a framework where teams of agents compete in a game of robotic soccer. The real system of model cars remotely controlled from off-board computers is under development.

Our research works towards this broad goal from a Machine Learning perspective. We are particularly interested in investigating how an intelligent agentcan choose an action in an adversarial environment. We assume that the agent has a specific goal to achieve. We conduct this investigation in a framework whereteams of agents compete in a game of robotic soccer. The real system of model cars remotely controlled from off-board computers is under development.

This paper investigates learning in a lifelong context. Lifelong learning addresses situations in which a learner faces a whole stream of learning tasks.Such scenarios provide the opportunity to transfer knowledge across multiple learning tasks, in order to generalize more accurately from less training data. In this paper, several different approaches to lifelong learning are described, and applied in an object recognition domain. It is shown that across the board, lifelong learning approaches generalize consistently more accurately from less training data, by their ability to transfer knowledge across learning tasks. 1 Introduction Supervised learning is concerned with approximating an unknown function based on examples. Virtuallyall current approaches to supervised learning assume that one is given a set of input-output examples, denoted by X, which characterize an unknown function, denoted by f.