Learning object boundary detection from motion data

A significant barrier to applying the techniques of machine learning to the domain of object boundary detection is the need to obtain a large database of correctly labeled examples. Inspired by developmental psychology, this paper proposes that boundary detection can be learned from the output of a motion tracking algorithm that separates moving objects from their static surroundings. Motion segmentation solves the database problem by providing cheap, unlimited, labeled training data. A probabilistic model of the textural and shape properties of object boundaries can be trained from this data and then used to efficiently detect boundaries in novel images via loopy belief propagation.Singapore-MIT Alliance (SMA

Learning Three-Dimensional Shape Models for Sketch Recognition

Artifacts made by humans, such as items of furniture and houses, exhibit an enormous amount of variability in shape. In this paper, we concentrate on models of the shapes of objects that are made up of fixed collections of sub-parts whose dimensions and spatial arrangement exhibit variation. Our goals are: to learn these models from data and to use them for recognition. Our emphasis is on learning and recognition from three-dimensional data, to test the basic shape-modeling methodology. In this paper we also demonstrate how to use models learned in three dimensions for recognition of two-dimensional sketches of objects.Singapore-MIT Alliance (SMA

Playing is believing: the role of beliefs in multi-agent learning

We propose a new classification for multi-agent learning algorithms, with each league of players characterized by both their possible strategies and possible beliefs. Using this classification, we review the optimality of existing algorithms and discuss some insights that can be gained. We propose an incremental improvement to the existing algorithms that seems to achieve average payoffs that are at least the Nash equilibrium payoffs in the long-run against fair opponents.Singapore-MIT Alliance (SMA

Symbol acquisition for task-level planning

We consider the problem of how to plan efficiently in low-level, continuous state spaces with temporally abstract actions (or skills), by constructing abstract representations of the problem suitable for task-level planning.The central question this effort poses is which abstract representations are required to express and evaluate plans composed of sequences of skills. We show that classifiers can be used as a symbolic representation system, and that the ability to represent the preconditions and effects of an agent's skills is both necessary and sufficient for task-level planning.The resulting representations allow a reinforcement learning agent to acquire a symbolic representation appropriate for planning from experience

Symbol acquisition for probabilistic high-level planning

We introduce a framework that enables an agent to autonomously learn its own symbolic representation of a low-level, continuous environment. Propositional symbols are formalized as names for probability distributions, providing a natural means of dealing with uncertain representations and probabilistic plans. We determine the symbols that are sufficient for computing the probability with which a plan will succeed, and demonstrate the acquisition of a symbolic representation in a computer game domain.National Science Foundation (U.S.) (grant 1420927)United States. Office of Naval Research (grant N00014-14-1-0486)United States. Air Force. Office of Scientific Research (grant FA23861014135)United States. Army Research Office (grant W911NF1410433)MIT Intelligence Initiativ

All learning is local: Multi-agent learning in global reward games

In large multiagent games, partial observability, coordination, and credit assignment persistently plague attempts to design good learning algorithms. We provide a simple and efficient algorithm that in part uses a linear system to model the world from a single agent’s limited perspective, and takes advantage of Kalman filtering to allow an agent to construct a good training signal and effectively learn a near-optimal policy in a wide variety of settings. A sequence of increasingly complex empirical tests verifies the efficacy of this technique.Singapore-MIT Alliance (SMA