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

Deductive synthesis of recursive plans in linear logic

Linear logic has previously been shown to be suitable for describing and deductively solving planning problems involving conjunction and disjunction. We introduce a recursively defined datatype and a corresponding induction rule, thereby allowing recursive plans to be synthesised. In order to make explicit the relationship between proofs and plans, we enhance the linear logic deduction rules to handle plans as a form of proof term

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

Developing manufacturing control software: A survey and critique

The complexity and diversity of manufacturing software and the need to adapt this software to the frequent changes in the production requirements necessitate the use of a systematic approach to developing this software. The software life-cycle model (Royce, 1970) that consists of specifying the requirements of a software system, designing, implementing, testing, and evolving this software can be followed when developing large portions of manufacturing software. However, the presence of hardware devices in these systems and the high costs of acquiring and operating hardware devices further complicate the manufacturing software development process and require that the functionality of this software be extended to incorporate simulation and prototyping.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45542/1/10696_2005_Article_BF01328739.pd

Enterocolitis necrotizante en recién nacidos ingresados en el Servicio de Neonatología del Hospital Escuela "Carlos Roberto Huembes" en el período comprendido de Enero 2011 a Diciembre 2013

La enterocolitis necrotizante en el recién nacido presenta un amplio espectro de manifestaciones clínicas, caracterizándose principalmente por la tríada de distensión abdominal, sangramiento gastrointestinal y neumatosis intestinal. A pesar del avance en el cuidado intensivo neonatal, persiste como una enfermedad grave, que afecta habitualmente al recién nacido pretérmino, especialmente de muy bajo peso