scholarly journals Pattern and Function Transfer for Learning Classifier Systems

2021 ◽  
Author(s):  
Trung Nguyen
Keyword(s):  
Building Blocks ◽  
Multitask Learning ◽  
Learning Performance ◽  
Classifier Systems ◽  
Parallel Learning ◽  
Learning Agents ◽  
Complex Problems ◽  
Learning Classifier ◽  
Layered Learning ◽  
High Level

<p><b>A key goal of Artificial Intelligence (AI) is to replicate different aspects of biological intelligence. Human intelligence can accumulate progressively complicated knowledge by reusing simpler concepts/tasks to represent more complex concepts and solve more difficult tasks. Also, humans and animals with biological intelligence have the autonomy that helps sustain them over a long period. </b></p><p>Young humans need a long period to obtain simple concepts and master basic skills. However, these learnt basic concepts and skills are important to construct foundation knowledge, which is highly reusable and thereby efficiently exploited to learn new knowledge. By relating unseen tasks to learnt knowledge, humans can learn new knowledge or solve new problems effectively. Thus, AI researchers aim to mimic human performance with the same ability to reuse learnt knowledge when solving a novel task in a continual manner. </p><p>Initial attempts to implement this knowledge-transfer ability have been through layered learning and multitask learning. Layered learning aims to learn a complex target task by learning a sequence of easier tasks that provide supportive knowledge prior to learning the target task. This learning paradigm requires human knowledge that may be biased, costly, or not available in a particular domain. Multitask learning generally uses multiple related tasks with individual goals to be learnt together with the hope that they can provide externally supportive signals to each other. However, multitask learning is commonly applied to optimisation tasks that are required to start simultaneously. </p><p>In this thesis, using the transfer of building blocks of learnt knowledge is of interest to solve complex problems. A complex problem is one where the solution cannot be simply enumerated in the time and computation available, often because there are multiple interacting patterns of input features or high dimensions in the data. A strategy for solving complex problems is to discover the high-level patterns in the data. The high-level patterns are ones with complex combinations of original input features (the underlying building blocks) to describe the desired output. However, as the complexity of building blocks grows along with the problem complexity, the size of the search space for solutions and the optimal building blocks also increases in complexity. This poses a challenge in discovering optimal building blocks. </p><p>Learning Classifier Systems (LCSs) are evolutionary rule-based algorithms inspired by cognitive science. LCSs are of interest as their niching nature enables solving problems heterogeneously and learning them progressively from simpler subproblems to more complex (sub)problems. LCSs also encourage transferring subproblem building blocks among tasks. Recent work has extended LCSs with various flexible representations. Among them, Code Fragments (CFs), Genetic Programming (GP)-like trees, are a rich form that can encode complex patterns in a small and concise format. CF-based LCSs are particularly suitable for addressing complex problems. For example, XCSCF*, which was based on Wilson's XCS (an accuracy-based online learning LCS), can learn a generalised solution to the n-bit Multiplexer problem. The above techniques provided remarkable improvements to the scalability of CF-based LCSs. However, there are certain limits in such systems compared with human intelligence, such as their limited autonomy, e.g. the requirement of an appropriate learning order (e.g. layered learning) to enable learning progress. Humans can learn multiple tasks in a parallel ad hoc manner, whereas AI cannot do this autonomously. </p><p>The proposed thesis is that systems of parallel learning agents can solve multiple problems concurrently enabling multitask learning and eventually the ability to learn continually. Here, each agent is a CF-based XCS where the problems are Boolean in nature to aid interpretability. The overall goal of this thesis is to develop novel CF-based XCSs that enable learning continually with the least human support. </p><p>The contributions of this thesis are three specific systems that provide a pathway to continual learning. By reducing the requirements of human guidance without degrading the learning performance. (1) The evolution of CFs is nested and interactive with the evolution of rules. The fitness of CFs called CF-fitness is introduced to guide this process. The evolution of CFs enables growing the complexity of CFs without a depth limit to address hierarchical features. The system is the first XCS with CFs in rule conditions that can learn complex problems that used to be intractable without transfer learning. The introduction of CF evolution allows appropriate latent building blocks that address subproblems to be grouped together and flexibly reused. (2) A new system of multitask learning is developed based on the estimation of the relatedness among tasks. A new dynamic parameter helps automate feature transfer among multiple tasks, which enables improved learning performance in supportive tasks and reduced negative influence between unrelated tasks. (3) A system of parallel learning agents, where each is an XCS with CF-actions, is developed to remove the requirement of a human-biased learning order. The system can provide a clear learning order and a highly interpretable network of knowledge. This network of knowledge enables the system to accumulate knowledge hierarchically and focus on only the novel aspects of any new task. </p><p>The research work has shown that CF-based LCSs can solve hierarchical and large-scale problems autonomously without (extensive) human guidance. The learnt knowledge represented by CFs is highly interpretable. This work is also a foundation for the systems that can learn continually. Ultimately, this thesis is a step towards general learners and problem solvers.</p>

scholarly journals Pattern and Function Transfer for Learning Classifier Systems

2021 ◽  
Author(s):  
Trung Nguyen
Keyword(s):  
Building Blocks ◽  
Multitask Learning ◽  
Learning Performance ◽  
Classifier Systems ◽  
Parallel Learning ◽  
Learning Agents ◽  
Complex Problems ◽  
Learning Classifier ◽  
Layered Learning ◽  
High Level

<p><b>A key goal of Artificial Intelligence (AI) is to replicate different aspects of biological intelligence. Human intelligence can accumulate progressively complicated knowledge by reusing simpler concepts/tasks to represent more complex concepts and solve more difficult tasks. Also, humans and animals with biological intelligence have the autonomy that helps sustain them over a long period. </b></p><p>Young humans need a long period to obtain simple concepts and master basic skills. However, these learnt basic concepts and skills are important to construct foundation knowledge, which is highly reusable and thereby efficiently exploited to learn new knowledge. By relating unseen tasks to learnt knowledge, humans can learn new knowledge or solve new problems effectively. Thus, AI researchers aim to mimic human performance with the same ability to reuse learnt knowledge when solving a novel task in a continual manner. </p><p>Initial attempts to implement this knowledge-transfer ability have been through layered learning and multitask learning. Layered learning aims to learn a complex target task by learning a sequence of easier tasks that provide supportive knowledge prior to learning the target task. This learning paradigm requires human knowledge that may be biased, costly, or not available in a particular domain. Multitask learning generally uses multiple related tasks with individual goals to be learnt together with the hope that they can provide externally supportive signals to each other. However, multitask learning is commonly applied to optimisation tasks that are required to start simultaneously. </p><p>In this thesis, using the transfer of building blocks of learnt knowledge is of interest to solve complex problems. A complex problem is one where the solution cannot be simply enumerated in the time and computation available, often because there are multiple interacting patterns of input features or high dimensions in the data. A strategy for solving complex problems is to discover the high-level patterns in the data. The high-level patterns are ones with complex combinations of original input features (the underlying building blocks) to describe the desired output. However, as the complexity of building blocks grows along with the problem complexity, the size of the search space for solutions and the optimal building blocks also increases in complexity. This poses a challenge in discovering optimal building blocks. </p><p>Learning Classifier Systems (LCSs) are evolutionary rule-based algorithms inspired by cognitive science. LCSs are of interest as their niching nature enables solving problems heterogeneously and learning them progressively from simpler subproblems to more complex (sub)problems. LCSs also encourage transferring subproblem building blocks among tasks. Recent work has extended LCSs with various flexible representations. Among them, Code Fragments (CFs), Genetic Programming (GP)-like trees, are a rich form that can encode complex patterns in a small and concise format. CF-based LCSs are particularly suitable for addressing complex problems. For example, XCSCF*, which was based on Wilson's XCS (an accuracy-based online learning LCS), can learn a generalised solution to the n-bit Multiplexer problem. The above techniques provided remarkable improvements to the scalability of CF-based LCSs. However, there are certain limits in such systems compared with human intelligence, such as their limited autonomy, e.g. the requirement of an appropriate learning order (e.g. layered learning) to enable learning progress. Humans can learn multiple tasks in a parallel ad hoc manner, whereas AI cannot do this autonomously. </p><p>The proposed thesis is that systems of parallel learning agents can solve multiple problems concurrently enabling multitask learning and eventually the ability to learn continually. Here, each agent is a CF-based XCS where the problems are Boolean in nature to aid interpretability. The overall goal of this thesis is to develop novel CF-based XCSs that enable learning continually with the least human support. </p><p>The contributions of this thesis are three specific systems that provide a pathway to continual learning. By reducing the requirements of human guidance without degrading the learning performance. (1) The evolution of CFs is nested and interactive with the evolution of rules. The fitness of CFs called CF-fitness is introduced to guide this process. The evolution of CFs enables growing the complexity of CFs without a depth limit to address hierarchical features. The system is the first XCS with CFs in rule conditions that can learn complex problems that used to be intractable without transfer learning. The introduction of CF evolution allows appropriate latent building blocks that address subproblems to be grouped together and flexibly reused. (2) A new system of multitask learning is developed based on the estimation of the relatedness among tasks. A new dynamic parameter helps automate feature transfer among multiple tasks, which enables improved learning performance in supportive tasks and reduced negative influence between unrelated tasks. (3) A system of parallel learning agents, where each is an XCS with CF-actions, is developed to remove the requirement of a human-biased learning order. The system can provide a clear learning order and a highly interpretable network of knowledge. This network of knowledge enables the system to accumulate knowledge hierarchically and focus on only the novel aspects of any new task. </p><p>The research work has shown that CF-based LCSs can solve hierarchical and large-scale problems autonomously without (extensive) human guidance. The learnt knowledge represented by CFs is highly interpretable. This work is also a foundation for the systems that can learn continually. Ultimately, this thesis is a step towards general learners and problem solvers.</p>

scholarly journals Developing the Capability to Scale and Reuse Learned Knowledge and Functionality in Learning Classifier Systems

2021 ◽  
Author(s):  
◽  
Isidro M. Alvarez
Keyword(s):  
General Solution ◽  
Building Blocks ◽  
Learning Classifier Systems ◽  
Classifier Systems ◽  
General Solutions ◽  
Complex Problems ◽  
Learning Classifier ◽  
Evaluation Time ◽  
Layered Learning ◽  
Intractable Problem

<p>Learning is an important activity through which humanity has incrementally improved accomplishing tasks by adapting knowledge and methods based on the related feedback. Although learning is natural to humans, it is much more difficult to achieve in the technological world as tasks are often learned in isolation. Software is capable of learning novel techniques and algorithms in order to solve these basic, individual problems, however transferring said knowledge to other problems in the same or related domains presents challenges. Solutions often cannot be enumerated to discover the best one as many problems of interest can be intractable in terms of the resources needed to successfully complete them. However, many such problems contain key building blocks of knowledge that can be leveraged to achieve a suitable solution. These building blocks encapsulate important structural regularities of the problem. A technique that can learn these regularities without enumeration,may produce general solutions that apply to similar problems of any length. This implies reusing learned information.  In order to reuse learned blocks of knowledge, it is important that a program be scalable and flexible. This requires a program capable of taking knowledge from a previous task and applying it to a more complex problem or a problem with a similar pattern. This is anticipated to enable the program to complete the new task in a practical amount of time and with reasonable amounts of resources.  In machine learning, the degree of human intervention in solving problems is often important in many tasks. It is generally necessary for a human to provide input to direct and improve learning. In the field of Developmental Learning there is the idea known as the Threshold Concept (TC). A TC is transformative information which advocates learning. TCs are important because without them, the learner cannot progress. In addition, TCs need to be learned in a particular order, much like a curriculum, thus providing the student with viable progress towards learning more difficult ideas at a faster pace than otherwise. Therefore, human input to a learning algorithm can be to partition a problem into constituent subproblems. This is a principal concept of Layered Learning (LL),where a sequence of sub-problems are learned. The sub-problems are self-contained stages which have been separated by a human. This technique is necessary for tasks in which learning a direct mapping from inputs to outputs is intractable given existing learning algorithms.  One of the first artificial learning systems developed is Learning Classifier Systems (LCSs). Past work has extended LCSs to provide more expressivity by using richer representations. One such representation is tree-based and is common to the Genetic Programming (GP) technique. GP is part of the Evolutionary Computation (EC) paradigm and produces solutions represented by trees. The tree nodes can contain functions, and leaf nodes problem features, giving GP a rich representation. A more recent technique is Code Fragments (CFs). CFs are GP-like sub-trees with an initial maximum height of two. Initially, CFs contained hard-coded functions at the root nodes and problem features or previously learned CFs at the leaf nodes of the sub-trees. CFs provided improved expressivity and scalability over the original ternary alphabet used by LCSs. Additionally, CF-based systems have successfully learned previously intractable problems, e.g. 135-bit multiplexer.  Although CFs have provided increased scalability, they suffer from a structural weakness. As the problem scales, the chains of CFs grow to intractable lengths. This means that at some point the LCS will stop learning. In addition, CFs were originally meant to scale to more complex problems in the same domain. However, it is advantageous to compile cross-domain solutions, as the regularities of a problem might be from different domains to that expressed by the data.  The proposed thesis is that a CF-based LCS can scale to complex problems by reusing learned solutions of problems as functions at the inner nodes of CFs together with compaction and Layered Learning. The overall goal is divided into the following three sub-goals: reuse learned functionality from smaller problems in the root nodes of CF sub-trees, identify a compaction technique that facilitates reduced solution size for improved evaluation time of CFs and develop a layered learning methodology for a CF system, which will be demonstrated by learning a general solution to an intractable problem, i.e. n-bit Multiplexer.  In this novel work, Code Fragments are extended to include learned functionality at the root nodes of the sub-trees in a technique known as XCSCF². A new compaction technique is designed, which produces an equivalent set of ternary rules from CF rules. This technique is known as XCSCF3. The work culminates with a new technique XCSCF*, which combines Layered Learning, Code Fragments and Transfer Learning (TL) of knowledge and functionality to produce scalable and general solutions, i.e. to the n-bit multiplexer problem.  The novel ideas are tested with the multiplexer and hidden multiplexer problems. These problems are chosen because they are difficult due to epistasis, sparsity and non-linearity. Therefore they provide ample opportunity for testing the new contributions.  The thesis work has shown that CFs can be used in various ways to increase scalability and to discover solutions to complex problems. Specifically the following three contributions were produced: learned functionality was captured in LCS populations from smaller problems and was reused in the root nodes of CF sub-trees. An online compaction technique that facilitates reduced evaluation time of CFs was designed. A layered learning method to train a CF system in a manner leading to a general solution was developed. This was demonstrated through learning a solution to a previously intractable problem, i.e. the n-bit Multiplexer. The thesis concludes with suggestions for future work aimed at providing better scalability when using compaction techniques.</p>

scholarly journals Developing the Capability to Scale and Reuse Learned Knowledge and Functionality in Learning Classifier Systems

2021 ◽  
Author(s):  
◽  
Isidro M. Alvarez
Keyword(s):  
General Solution ◽  
Building Blocks ◽  
Learning Classifier Systems ◽  
Classifier Systems ◽  
General Solutions ◽  
Complex Problems ◽  
Learning Classifier ◽  
Evaluation Time ◽  
Layered Learning ◽  
Intractable Problem

<p>Learning is an important activity through which humanity has incrementally improved accomplishing tasks by adapting knowledge and methods based on the related feedback. Although learning is natural to humans, it is much more difficult to achieve in the technological world as tasks are often learned in isolation. Software is capable of learning novel techniques and algorithms in order to solve these basic, individual problems, however transferring said knowledge to other problems in the same or related domains presents challenges. Solutions often cannot be enumerated to discover the best one as many problems of interest can be intractable in terms of the resources needed to successfully complete them. However, many such problems contain key building blocks of knowledge that can be leveraged to achieve a suitable solution. These building blocks encapsulate important structural regularities of the problem. A technique that can learn these regularities without enumeration,may produce general solutions that apply to similar problems of any length. This implies reusing learned information.  In order to reuse learned blocks of knowledge, it is important that a program be scalable and flexible. This requires a program capable of taking knowledge from a previous task and applying it to a more complex problem or a problem with a similar pattern. This is anticipated to enable the program to complete the new task in a practical amount of time and with reasonable amounts of resources.  In machine learning, the degree of human intervention in solving problems is often important in many tasks. It is generally necessary for a human to provide input to direct and improve learning. In the field of Developmental Learning there is the idea known as the Threshold Concept (TC). A TC is transformative information which advocates learning. TCs are important because without them, the learner cannot progress. In addition, TCs need to be learned in a particular order, much like a curriculum, thus providing the student with viable progress towards learning more difficult ideas at a faster pace than otherwise. Therefore, human input to a learning algorithm can be to partition a problem into constituent subproblems. This is a principal concept of Layered Learning (LL),where a sequence of sub-problems are learned. The sub-problems are self-contained stages which have been separated by a human. This technique is necessary for tasks in which learning a direct mapping from inputs to outputs is intractable given existing learning algorithms.  One of the first artificial learning systems developed is Learning Classifier Systems (LCSs). Past work has extended LCSs to provide more expressivity by using richer representations. One such representation is tree-based and is common to the Genetic Programming (GP) technique. GP is part of the Evolutionary Computation (EC) paradigm and produces solutions represented by trees. The tree nodes can contain functions, and leaf nodes problem features, giving GP a rich representation. A more recent technique is Code Fragments (CFs). CFs are GP-like sub-trees with an initial maximum height of two. Initially, CFs contained hard-coded functions at the root nodes and problem features or previously learned CFs at the leaf nodes of the sub-trees. CFs provided improved expressivity and scalability over the original ternary alphabet used by LCSs. Additionally, CF-based systems have successfully learned previously intractable problems, e.g. 135-bit multiplexer.  Although CFs have provided increased scalability, they suffer from a structural weakness. As the problem scales, the chains of CFs grow to intractable lengths. This means that at some point the LCS will stop learning. In addition, CFs were originally meant to scale to more complex problems in the same domain. However, it is advantageous to compile cross-domain solutions, as the regularities of a problem might be from different domains to that expressed by the data.  The proposed thesis is that a CF-based LCS can scale to complex problems by reusing learned solutions of problems as functions at the inner nodes of CFs together with compaction and Layered Learning. The overall goal is divided into the following three sub-goals: reuse learned functionality from smaller problems in the root nodes of CF sub-trees, identify a compaction technique that facilitates reduced solution size for improved evaluation time of CFs and develop a layered learning methodology for a CF system, which will be demonstrated by learning a general solution to an intractable problem, i.e. n-bit Multiplexer.  In this novel work, Code Fragments are extended to include learned functionality at the root nodes of the sub-trees in a technique known as XCSCF². A new compaction technique is designed, which produces an equivalent set of ternary rules from CF rules. This technique is known as XCSCF3. The work culminates with a new technique XCSCF*, which combines Layered Learning, Code Fragments and Transfer Learning (TL) of knowledge and functionality to produce scalable and general solutions, i.e. to the n-bit multiplexer problem.  The novel ideas are tested with the multiplexer and hidden multiplexer problems. These problems are chosen because they are difficult due to epistasis, sparsity and non-linearity. Therefore they provide ample opportunity for testing the new contributions.  The thesis work has shown that CFs can be used in various ways to increase scalability and to discover solutions to complex problems. Specifically the following three contributions were produced: learned functionality was captured in LCS populations from smaller problems and was reused in the root nodes of CF sub-trees. An online compaction technique that facilitates reduced evaluation time of CFs was designed. A layered learning method to train a CF system in a manner leading to a general solution was developed. This was demonstrated through learning a solution to a previously intractable problem, i.e. the n-bit Multiplexer. The thesis concludes with suggestions for future work aimed at providing better scalability when using compaction techniques.</p>

scholarly journals Identification of biological mechanisms by semantic classifier systems

2018 ◽  
Author(s):  
Ludwig Lausser ◽  
Florian Schmid ◽  
Lea Siegle ◽  
Rolf Hühne ◽  
Malte Buchholz ◽  
...  
Keyword(s):  
Data Mining ◽  
Domain Knowledge ◽  
De Novo ◽  
Building Blocks ◽  
Classification Model ◽  
Biological Knowledge ◽  
Classifier Systems ◽  
Semantic Approach ◽  
Classifier System ◽  
High Level

AbstractThe interpretability of a classification model is one of its most essential characteristics. It allows for the generation of new hypotheses on the molecular background of a disease. However, it is questionable if more complex molecular regulations can be reconstructed from such limited sets of data. To bridge the gap between complexity and interpretability, we replace the de novo reconstruction of these processes by a hybrid classification approach partially based on existing domain knowledge. Using semantic building blocks that reflect real biological processes these models were able to construct hypotheses on the underlying genetic configuration of the analysed phenotypes. As in the building process, also these hypotheses are composed of high-level biology-based terms. The semantic information we utilise from gene ontology is a vocabulary which comprises the essential processes or components of a biological system. The constructed semantic multi-classifier system consists of expert base classifiers which each select the most suitable term for characterising their assigned problems. Our experiments conducted on datasets of three distinct research fields revealed terms with well-known associations to the analysed context. Furthermore, some of the chosen terms do not seem to be obviously related to the issue and thus lead to new, hypotheses to pursue.Author summaryData mining strategies are designed for an unbiased de novo analysis of large sample collections and aim at the detection of frequent patterns or relationships. Later on, the gained information can be used to characterise diagnostically relevant classes and for providing hints to the underlying mechanisms which may cause a specific phenotype or disease. However, the practical use of data mining techniques can be restricted by the available resources and might not correctly reconstruct complex relationships such as signalling pathways.To counteract this, we devised a semantic approach to the issue: a multi-classifier system which incorporates existing biological knowledge and returns interpretable models based on these high-level semantic terms. As a novel feature, these models also allow for qualitative analysis and hypothesis generation on the molecular processes and their relationships leading to different phenotypes or diseases.

scholarly journals E-learning-Oriented Software Architecture Design and Case Study

2015 ◽  
Vol 10 (4) ◽  
pp. 59
Author(s):  
Peng Lu ◽  
Xiao Cong ◽  
Dongdai Zhou
Keyword(s):  
Software Architecture ◽  
Building Blocks ◽  
Domain Model ◽  
Learning System ◽  
Application Framework ◽  
Component Library ◽  
E Learning ◽  
The Difference ◽  
High Level

Nowadays, E-learning system has been widely applied to practical teaching. It was favored by people for its characterized course arrangement and flexible learning schedule. However, the system does have some problems in the process of application such as the functions of single software are not diversified enough to satisfy the requirements in teaching completely. In order to cater more applications in the teaching process, it is necessary to integrate functions from different systems. But the difference in developing techniques and the inflexibility in design makes it difficult to implement. The major reason of these problems is the lack of fine software architecture. In this article, we build domain model and component model of E-learning system and components integration method on the basis of WebService. And we proposed an abstract framework of E-learning which could express the semantic relationship among components and realize high level reusable on the basis of informationized teaching mode. On this foundation, we form an E-learning oriented layering software architecture contain component library layer, application framework layer and application layer. Moreover, the system contains layer division multiplexing and was not built upon developing language and tools. Under the help of the software architecture, we could build characterized E-learning system flexibly like building blocks through framework selection, component assembling and replacement. In addition, we exemplify how to build concrete E-learning system on the basis of this software architecture.

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