Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

語用論(Pragmatics)を考える

https://dialnet.unirioja.es/descarga/articulo/4511387.pdf

The origins of order. Self-organization and selection in evolution

http://user.it.uu.se/~jarst116/slides/week4.pdf

Graph-Based Algorithms for Boolean Function Manipulation

This book constitutes the thoroughly refereed post-proceedings of the 23rd International Conference on Inductive Logic Programming, ILP 2013, held in Rio de Janeiro, Brazil, in August 2013. The 9 revised extended papers were carefully reviewed and selected from 42 submissions. The conference now focuses on all aspects of learning in logic, multi-relational learning and data mining, statistical relational learning, graph and tree mining, relational reinforcement learning, and other forms of learning from structured data.

/pdf/inductive-logic-programming-23rd-international-conference-2c79b4mf0y.pdf

Inductive Logic Programming: 23rd International Conference, ILP 2013, Rio de Janeiro, Brazil, August 28-30, 2013, Revised Selected Papers

Probabilistic planners have improved recently to the point that they can solve difficult tasks with complex and expressive models. In contrast, learners cannot tackle yet the expressive models that planners do, which forces complex models to be mostly handcrafted. We propose a new learning approach that can learn relational probabilistic models with both action effects and exogenous effects. The proposed learning approach combines a multi-valued variant of inductive logic programming for the generation of candidate models, with an optimization method to select the best set of planning operators to model a problem. We also show how to combine this learner with reinforcement learning algorithms to solve complete problems. Finally, experimental validation is provided that shows improvements over previous work in both simulation and a robotic task. The robotic task involves a dynamic scenario with several agents where a manipulator robot has to clear the tableware on a table. We show that the exogenous effects learned by our approach allowed the robot to clear the table in a more efficient way.

https://jmlr.org/papers/volume18/16-326/16-326.pdf

Relational Reinforcement Learning for Planning with Exogenous Effects

We propose a novel framework for learning normal logic programs from transitions of interpretations. Given a set of pairs of interpretations (I,J) such that J=TP(I), where TP is the immediate consequence operator, we infer the program P. The learning framework can be repeatedly applied for identifying Boolean networks from basins of attraction. Two algorithms have been implemented for this learning task, and are compared using examples from the biological literature. We also show how to incorporate background knowledge and inductive biases, then apply the framework to learning transition rules of cellular automata.

/pdf/learning-from-interpretation-transition-5b9f8uoo6p.pdf

Learning from interpretation transition

How to form a team for achieving a given set of tasks is an important issue in multi-agent systems. Task-oriented team formation is the problem of selecting a group of agents, where each agent is characterized by a set of capabilities; the objective is to achieve a given set of tasks, where each task is made precise by a set of capabilities necessary for managing it. Robustness (i.e., the ability to reach the goal even if some agents break down) is an expected property of a team. In this paper, the focus is laid on the Task-Oriented Robust Team Formation (TORTF) problem. A formal framework is defined and some decision and optimization problems for TORTF are pointed out. The computational complexity of TORTF is then identified. Interestingly, TORTF does not prove more computationally demanding than the task-efficient team formation problem, i.e., robustness is in some sense "for free". In order to solve these TORTF problems, two algorithms, ART (Algorithm for Robust Team) for the decision problem and AORT (Algorithm for Optimal Robust Team) for bi-objective constraint optimization problems, are presented and evaluated on a number of benchmarks.

How to Form a Task-Oriented Robust Team

In a previous work we proposed a framework for learning normal logic programs from transitions of interpretations. Given a set of pairs of interpretations I,i¾?J such that $$J=T_PI$$, where $$T_P$$ is the immediate consequence operator, we infer the program P. Here we propose a new learning approach that is more efficient in terms of output quality. This new approach relies on specialization in place of generalization. It generates hypotheses by specialization from the most general clauses until no negative transition is covered. Contrary to previous approaches, the output of this method does not depend on variables/transitions ordering. The new method guarantees that the learned rules are minimal, that is, the body of each rule constitutes a prime implicant to infer the head.

/pdf/learning-prime-implicant-conditions-from-interpretation-4345lwyj0h.pdf

Learning Prime Implicant Conditions from Interpretation Transition

Many researchers of different fields are interested in building resilient systems that can absorb shocks and recover from damages caused by unexpected large-scale events; however, no common agreement on the definition of resilience exists. In this paper, we set out to establish a new challenging research discipline that we call "systems resilience", which provides a set of unified design principles for building resilient systems. We define a dynamic constraint-based agent model called SR-model, whose resilience can be evaluated through a range of properties.

/pdf/systems-resilience-a-challenge-problem-for-dynamic-7vxi77mncb.pdf

Tony Ribeiro

Papers

Relational Reinforcement Learning for Planning with Exogenous Effects

Learning from interpretation transition

How to Form a Task-Oriented Robust Team

Learning Prime Implicant Conditions from Interpretation Transition

Systems resilience: a challenge problem for dynamic constraint-based agent systems