The Apriori Stochastic Dependency Detection (ASDD) algorithm for learning Stochastic logic rules

Apriori Stochastic Dependency Detection (ASDD) is an algorithm for fast induction of stochastic logic rules from a database of observations made by an agent situated in an environment. ASDD is based on features of the Apriori algorithm for mining association rules in large databases of sales transactions [1] and the MSDD algorithm for discovering stochastic dependencies in multiple streams of data [15]. Once these rules have been acquired the Precedence algorithm assigns operator precedence when two or more rules matching the input data are applicable to the same output variable. These algorithms currently learn propositional rules, with future extensions aimed towards learning first-order models. We show that stochastic rules produced by this algorithm are capable of reproducing an accurate world model in a simple predator-prey environment

SMART (Stochastic Model Acquisition with ReinforcemenT) learning agents: A preliminary report

We present a framework for building agents that learn using SMART, a system that combines stochastic model acquisition with reinforcement learning to enable an agent to model its environment through experience and subsequently form action selection policies using the acquired model. We extend an existing algorithm for automatic creation of stochastic strips operators [9] as a preliminary method of environment modelling. We then define the process of generation of future states using these operators and an initial state and finally show the process by which the agent can use the generated states to form a policy with a standard reinforcement learning algorithm. The potential of SMART is exemplified using the well-known predator prey scenario. Results of applying SMART to this environment and directions for future work are discussed

Periodic and Quasiperiodic Motion of an Elongated Microswimmer in Poiseuille Flow

We study the dynamics of a prolate spheroidal microswimmer in Poiseuille flow for different flow geometries. When moving between two parallel plates or in a cylindrical microchannel, the swimmer performs either periodic swinging or periodic tumbling motion. Although the trajectories of spherical and elongated swimmers are qualitatively similar, the swinging and tumbling frequency strongly depends on the aspect ratio of the swimmer. In channels with reduced symmetry the swimmers perform quasiperiodic motion which we demonstrate explicitely for swimming in a channel with elliptical cross section

Charged-particle distributions in √s=13 TeV pp interactions measured with the ATLAS detector at the LHC

Charged-particle distributions are measured in proton–proton collisions at a centre-of-mass energy of 13 TeV, using a data sample of nearly 9 million events, corresponding to an integrated luminosity of 170 μb−1170 μb−1, recorded by the ATLAS detector during a special Large Hadron Collider fill. The charged-particle multiplicity, its dependence on transverse momentum and pseudorapidity and the dependence of the mean transverse momentum on the charged-particle multiplicity are presented. The measurements are performed with charged particles with transverse momentum greater than 500 MeV and absolute pseudorapidity less than 2.5, in events with at least one charged particle satisfying these kinematic requirements. Additional measurements in a reduced phase space with absolute pseudorapidity less than 0.8 are also presented, in order to compare with other experiments. The results are corrected for detector effects, presented as particle-level distributions and are compared to the predictions of various Monte Carlo event generators

Charged-particle distributions at low transverse momentum in √<i>s</i>=13 TeV <i>pp</i> interactions measured with the ATLAS detector at the LHC

Measurements of distributions of charged particles produced in proton-proton collisions with a centre-of-mass energy of 13 TeV are presented. The data were recorded by the ATLAS detector at the LHC and correspond to an integrated luminosity of 151 [Formula: see text]. The particles are required to have a transverse momentum greater than 100 MeV and an absolute pseudorapidity less than 2.5. The charged-particle multiplicity, its dependence on transverse momentum and pseudorapidity and the dependence of the mean transverse momentum on multiplicity are measured in events containing at least two charged particles satisfying the above kinematic criteria. The results are corrected for detector effects and compared to the predictions from several Monte Carlo event generators

Integrating Unsupervised Learning, Motivation and Action Selection in an A-life Agent

How can we expect an A-life Agent to learn how to perform tasks when it is not told what those tasks are, and it is not provided any indication or feedback as to its performance? This is at the heart of the unsupervised learning problem. If the Agent were able to learn in this manner, how could specific tasks be communicated to it? This is the Goal setting problem. Having been set a task, how would the Agent go about choosing things to do that will lead it to perform those tasks in an orderly manner? This is at the heart of the action selection problem.

TRACHOMA

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (p. 95-96).If we are to understand human-level intelligence, we need to understand how meanings can be learned without explicit instruction. I take a step toward that understanding by focusing on the symbol-grounding problem, showing how symbols can emerge from a system that looks for regularity in the experiences of its visual and proprioceptive sensory systems. More specifically, my implemented system builds descriptions up from low-level perceptual information and, without supervision, discovers regularities in that information. Then, my system, with supervision, associates the regularity with symbolic tags. Experiments conducted with the implementation shows that it successfully learns symbols corresponding to blocks in a simple 2D blocks world, and learns to associate the position of its eye with the position of its arm. In the course of this work, I take a new perspective on how to design knowledge representations, one that grapples with the internal semantics of systems, and I propose a model of an adaptive knowledge representation scheme that is intrinsic to the model and not parasitic on meanings captured in some external system, such as the head of a human investigator.by Stephen David Larson.M.Eng