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

Membrane computing is a well-known research area in computer science inspired by the organization and behavior of live cells and tissues. Their computational devices, called P systems, work in parallel and distributed mode and the information is encoded by multisets in a localized manner. All these features make P systems appropriate for dealing with digital images. In this paper, some of the open research lines in the area are presented, focusing on segmentation problems, skeletonization and algebraic-topological aspects of the images. An extensive bibliography about the application of membrane computing to the study of digital images is also provided.

Membrane computing and image processing: a short survey

Tissue $P$ systems with promoters provide nondeterministic parallel bioinspired devices that evolve by the interchange of objects between regions, determined by the existence of some special objects called promoters . However, in cellular biology, the movement of molecules across a membrane is transported from high to low concentration. Inspired by this biological fact, in this article, an interesting type of tissue $P$ systems, called monodirectional tissue $P$ systems with promoters , where communication happens between two regions only in one direction, is considered. Results show that finite sets of numbers are produced by such $P$ systems with one cell, using any length of symport rules or with any number of cells, using a maximal length 1 of symport rules, and working in the maximally parallel mode. Monodirectional tissue $P$ systems are Turing universal with two cells, a maximal length 2, and at most one promoter for each symport rule, and working in the maximally parallel mode or with three cells, a maximal length 1, and at most one promoter for each symport rule, and working in the flat maximally parallel mode. We also prove that monodirectional tissue $P$ systems with two cells, a maximal length 1, and at most one promoter for each symport rule (under certain restrictive conditions) working in the flat maximally parallel mode characterizes regular sets of natural numbers. Besides, the computational efficiency of monodirectional tissue $P$ systems with promoters is analyzed when cell division rules are incorporated. Different uniform solutions to the Boolean satisfiability problem (SAT problem) are provided. These results show that with the restrictive condition of “monodirectionality,” monodirectional tissue $P$ systems with promoters are still computationally powerful. With the powerful computational power, developing membrane algorithms for monodirectional tissue $P$ systems with promoters is potentially exploitable.

Monodirectional Tissue P Systems With Promoters

The computational power of tissue-like P systems with promoters

The concept of a matter object being annihilated when meeting its corresponding anti-matter object is investigated in the context of membrane systems, i.e., of (distributed) multiset rewriting systems applying rules in the maximally parallel way. Computational completeness can be obtained with using only non-cooperative rules besides these matter/anti-matter annihilation rules if these annihilation rules have priority over the other rules. Without this priority condition, in addition catalytic rules with one single catalyst are needed to get computational completeness. Even deterministic systems are obtained in the accepting case. Universal P systems with a rather small number of rules – 57 for computing systems, 59 for generating and 53 for accepting systems – can be constructed when using non-cooperative rules together with matter/anti-matter annihilation rules having weak priority.

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Matter and Anti-Matter in Membrane Systems

Counting the number of cells obtained in an experiment is crucial in many areas in Biology. Nonetheless, this is usually performed by hand by the researcher due the intrinsic difficulty of the task. In this paper, we present a set of techniques for counting cells inspired in the treatment of Digital Images via tissue-like P systems with promoters.

Counting Cells with Tissue-like P Systems

Antimatter as a Frontier of Tractability in Membrane Computing

Smoothing is often used in Digital Imagery to reduce noise within an image. In this paper we present a Membrane Computing algorithm for smoothing 2D images in the framework of tissue-like P systems. The algorithm has been implemented by using a novel device architecture called CUDA, (Compute Unified Device Architecture). We present some examples, compare the obtained time and present some research lines for the future.

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Smoothing Problem in 2D Images with Tissue-like P Systems and Parallel Implementation

Skeletonizing Images by Using Spiking Neural P Systems

Usually, the changes produced in the membrane structure of a P system are considered side effects. The output of the computation is encoded as a multiset placed in a specific region and the membrane structure in the halting configuration is not considered important. In this paper we explore P systems where the target of the computation is the construction of a new membrane structure according its set of rules. The new membrane structure can be considered as the initial one of a new self-constructed P system. We focus on the self-construction of recognizer P systems and illustrates the definition with a study of the self-construction P systems working as decision trees for solving Machine Learning decision problems.

Francisco Peña Cantillana

Papers

Counting Cells with Tissue-like P Systems

Antimatter as a Frontier of Tractability in Membrane Computing

Smoothing Problem in 2D Images with Tissue-like P Systems and Parallel Implementation

Skeletonizing Images by Using Spiking Neural P Systems

Self-constructing Recognizer P Systems