Skills and Expertise
Research Items (21)
- Jan 2017
- IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI), 2017
- International Symposium on Applied Machine Intelligence and Informatics
Reinforcement Learning (RL) methods became popular decades ago and still maintain to be one of the mainstream topics in computational intelligence. Countless different RL methods and variants can be found in the literature, each one having its own advantages and disadvantages in a specific application domain. Representation of the revealed knowledge can be realized in several ways depending on the exact RL method, including e.g. simple discrete Q-tables, fuzzy rule-bases, artificial neural networks. Introducing interpolation within the knowledge-base allows the omission of less important, redundant information, while still keeping the system functional. A Fuzzy Rule Interpolation-based (FRI) RL method called FRIQ-learning is a method which possesses this feature. By omitting the unimportant, dependent fuzzy rules — emphasizing the cardinal entries of the knowledge representation — FRIQ-learning is also suitable for knowledge extraction. In this paper the fundamental concepts of FRIQ-learning and associated extensions of the method along with benchmarks will be discussed.
The concept of ‘Future Internet’, ‘Internet of Things’ and ‘3D Internet’ opens a novel way for modeling ethological test s by rebuilding models of human - animal interaction in an augmented environment as an interactive mixture of virtual actors and real human observers. On the one hand these experiments can serve as a proof of concept, as a kind of experimental validation of formal ethological models, but on the other hand they can also serve as examples for the ways a human can communicate with things (i.e.,with everyday objects) in a virtual environment (e.g. on the Internet). These kinds of experiments can also support Cognitive Infocommunication related research, the field that investigates how a human can co-evolve with artificially cognitive systems through infocommunications devices. The goal of the paper is to introduce an example for such an ethological test system, a possible way for embedding a prototype ethological model described as a fuzzy automaton in MATLAB to the 3D VirCA collaborative augmented reality environment. Some details of the applied ethological experiment paradigm developed for studying the dog - owner relationship in a standard laboratory procedure, as a demonstrative example for ethological model implementation, will also be discussed briefly in this paper.
This paper presents human-robot interaction interfaces based on ethological studies. An ethological test procedure was modeled with the application of a fuzzy rule interpolation based fuzzy automaton. This fuzzy automaton was loaded with rules formed from the extracted ethological knowledge. Using the behaviours supplied by the fuzzy automaton as conclusions, different interfaces can be defined for the incarnation of the model. The ethological test procedure and its modeling technique based on the fuzzy automaton will be shortly introduced in the paper, and then the various human-robot interfaces based on the former will be presented. These include interfaces of simulated environments and also interfaces as real robot hardware with their supplemental devices (sensors, cameras, etc.).
- Mar 2010
- Applied Machine Intelligence and Informatics (SAMI), 2010 IEEE 8th International Symposium on
- International Symposium on Applied Machine Intelligence and Informatics
A novel aspect of human-robot interaction (HRI) can be put on the basis, that the robot side is implemented on a state-machine (fuzzy automaton), which reacts the human intervention as a function of the robot state and the human action. This platform is suitable for implementing quite complicated action-reaction sequences, like the interaction of human and an animal, e.g. a behaviour of an animal companion to the human. According to this paradigm the robot can follow the existing biological examples and form inter-species interaction. The 20,000 year old human-dog relationship is a good example for this paradigm of the HRI, as interaction of different species. In this paper, for ethologically inspired HRI model implementation, a fuzzy model structure built upon the framework of low computational demand fuzzy rule interpolation (FRI) methods and fuzzy automaton is suggested. For demonstrating the applicability of the proposed structure, some components of a human-dog interaction FRI model, which also suitable for HRI, will be briefly introduced in this paper.
- May 2011
Fuzzy Rule Interpolation (FRI) methods are efficient structures for knowledge-representation with relatively few rules. In spite of their good knowledge representation efficiency, their high computational demand makes the FRI methods hardly suitable for embedded real-time applications, for which short reasoning time has a high importance. On the other hand, the fact that currently available devices have increased computational power gives the FRI methods an opportunity to appear in real-time embedded applications. Therefore, the need for a low-computation and lowresource- demand FRI method is emerging. The goalof this paper is to introduce some implementation details of such an FRI method, together with its brief time and space complexity analysis. The paper also gives some hints for further performance optimization possibilities.
Question - How Much Space Need to Install Linux OS?
As with many other related questions: it depends...
It depends on what is your goal or purpose to use Linux. To use it as a fully-featured desktop system with all the bells and whistles, you'll need much more space than using it as a server without a fancy graphical desktop and other desktop applications (LibreOffice, web browser etc.). Usually during the installation procedure you can choose which components / applications you would like to install, so you can adjust the installation to your exact needs.
Ubuntu suggests 25GB storage space for a typical desktop installation, and 1.5GB for using it as a server (the bare system, without your data). Embedded systems can do with much less space , e.g. Linux based WiFi routers usually contain pretty small root filesystems, typically 8-16-32-64 MB (most of the time these devices use a compressed filesystem, e.g. cramfs).
It is advised to install the system on its own separate disk partition, but in case it is not option for you, you could try e.g. Wubi (Windows-based Ubuntu Installer) to use your existing Windows partition without destroying it (can be a huge advantage if you're just testing). The drawback is that your Linux system will run slower because of the additional filesystem mapping layer.
BTW, I remember creating a custom system back in the old days, which booted the Linux kernel from a 1.44 MB floppy disk, then used a 32 MB (sic! megabytes) USB flash drive as the root filesystem. And it still had some free space for the system logs...
This paper introduces a way to control the Pong game automatically with the usage of FRIQ-learning (Fuzzy Rule Interpolation-based Q-learning). The FRIQ-learning method can be a solution to such a problem which has a small state-space. The system starts with an empty knowledge base and the system constructs the final rule-base during the simulation, based on a reward used to solve the task. This way the method can find the required rules using the feedback provided by the environment. To correctly solve the problem the reward-function should be carefully defined for the corresponding problem (handling of the paddle in the Pong game in this case). After determining the required specifications (e.g. the actions and the effects of the actions) we used the FRIQ-learning framework to build a simulation application. FRIQ-learning can gather the required knowledge automatically in the form of a fuzzy rule-base, therefore it can be applied to such a system where the process of the exact operation is unknown. Our main goal is to show that the FRIQ-learning method is suitable to solve this problem by automatically constructing a sparse rule-base for Pong.
Question - How can we develop our own linux distribution?
I suggest reading the "Linux from Scratch" (LFS) website, which will probably show you that making your own distro from the ground is a really complex task and requires lots and lots of time.
Most of the current Linux distributions are not built from scratch, but built up based on another Linux distro, e.g. Ubuntu is based on Debian, Linux Mint is based on Debian and Ubuntu, CentOS is basically RedHat, etc.
If you need just to change something in your Linux kernel (add a driver for some hardware, enable/disable some functionality, etc.), you can easily recompile the kernel, there's no need to change the whole distro for that. Either you can use the kernel source provided with your distribution, or you can get a vanilla kernel from www.kernel.org and configure and compile it for yourself. You'll need a little in-depth knowledge to get a working kernel this way, but there could be a plenty of advantages of compiling and using your own customized kernel.
Or if you just need an application which is not included (or another version is included) in your current distro, you can try compiling it from source, usually for wide-spread and up to date application that's fairly easy to do.
Question - What is the best open source monitoring tool for network active equipments ?
As already mentioned, Nagios is a useful tool for monitoring the state of network connected devices. There are many built-in scripts you can use to monitor different services on a target network device, but you can also write your own script to do some custom monitoring/state checking.
If you would like to make some nice statistics on the network traffic based on switch ports/network interfaces, you could try Cacti, or Munin for example. For these to work you should enable SNMP on the devices you would like to monitor, and the machine running Cacti/Munin/etc. will poll them periodically via the network using SNMP. You'll need to configure a web server for these to work (they all have a web-based user interface, but the data polling from the network devices are done independently from the webserver as simple scripts executed by cron ).
They run fine under any Linux distribution, but haven't tried them under Win.
- Jan 2015
The Fuzzy Rule Interpolation (FRI)-based Fuzzy Automaton is an efficient structure for describing complex behaviour models in a relatively simple manner. The goal of this paper is to introduce a novel declarative behaviour description language which is created for supporting special needs of ethologically inspired behaviour model definition. For the sake of simplicity, the grammar is created with as few keywords as possible, keeping the ability to describe complex behavioural patterns as well. The language is a declarative language mainly supporting the behaviour models built upon structures of interpolative fuzzy automata. The paper firstly presents the formal structure of the behaviour description language itself, then gives an overview of the interpreting and processing engine designed for the language. Finally, an application example, a definition of a set of behaviours and a simulated environment is also presented.
Question - How can we create OOM Situation(Out Of Memory) by using Lmbench?
You can manually ask the Linux kernel to perform an oomkill by triggering the appropriate system request, e.g.:
echo f > /proc/sysrq-trigger
The Virtual Collaboration Arena (VirCA) is a modular, easy to use 3D framework supporting the development of augmented (real and virtual) reality applications. To apply the services provided by VirCA, special VirCA interfaces are needed in the actual programming environment. Native interface does not exist for some special environments, like e.g. for MATLAB, which is a commonly used for implementing complex models. The goal of the paper is to introduce a possible method for interconnecting the prototype spatial ETO-MOTOR model implemented in MATLAB with the 3D VirCA augmented reality environment. This is achieved by a newly developed adapter application, which translates between the two environments and uses standardized network protocols for communication. The ETO-MOTOR is a behaviour model based upon ethological studies. In the case of the example application, the type of the ETO-MOTOR is a “spatial ETO-MOTOR”, i.e. it can directly send spatial coordinates or heading directions to the virtual actors of the model. The prototype of the spatial ETO-MOTOR is constructed as a Fuzzy Rule Interpolation (FRI) based fuzzy automaton. In the example of the paper the ETO-MOTOR describes the behaviour of a dog in an unknown environment. In the original version of the model implementation, it has a very simple proof-of-concept user interface, which is sufficient for basic testing only, but very far from real world experiences. The personal interaction with the model thanks to the 3D VirCA augmented reality environment can yield much more direct observations and hence more profit for ethologists. The developed MATLAB-VirCA connector application provided with this paper can be easily modified to suit the needs of other third party applications too.
- May 2011
- 4th International Conference on Human System Interactions
For implementing ethologically inspired robot behavior in this paper a platform based on fuzzy automaton (fuzzy state-machine) is suggested. It can react the human intervention as a function of the robot state and the human action. This platform is suitable for implementing quite complicated action-reaction sequences, like the interaction of human and an animal, e.g. a behavior of an animal companion to the human. The suggested fuzzy model structure built upon the framework of low computational demand Fuzzy Rule Interpolation (FRI) methods and fuzzy automaton. For demonstrating the applicability of the proposed structure, some components of an action-reaction FRI model, will be briefly introduced in this paper.
- Nov 2010
This paper presents a concrete implementation of the Fuzzy Rule Interpolation (FRI) method called `FIVE'. FIVE is an acronym for Fuzzy rule Interpolation based on Vague Environment. The method itself tends to be a fast and simple to use application oriented FRI technique. Therefore the main goal of this paper to study the speed performance issues of the implementation. First a brief introduction to the FRI methods is given, then the FIVE method is shortly presented. For supporting the future application of the method, the paper also contains implementation details and description of the application programming interface and data formats. For studying the performance issues of the implemented FIVE on a benchmark application, some module level performance results will be also introduced in the paper.
- Oct 2010
- Computational Intelligence in Engineering
Reinforcement Learning (RL) is a widely known topic in computational intelligence. In the RL concept the problem needed to be solved is hidden in the feedback of the environment, called rewards. Using these rewards the system can learn which action is considered to be the best choice in a given state. One of the most frequently used RL method is the Q-learning, which was originally introduced for discrete states and actions. Applying fuzzy reasoning, the method can be adapted for continuous environments, called Fuzzy Q-learning. An extension of the Fuzzy Q-learning method with the capability of handling sparse fuzzy rule bases is already introduced by the authors. The latter suggests a Fuzzy Rule Interpolation (FRI) method to be the reasoning method applied with Q-learning, called FRIQ-learning. The main goal of this paper is to introduce a method which can construct the requested FRI fuzzy model from scratch in a reduced size. The reduction is achieved by incremental creation of an intentionally sparse fuzzy rule base. Moreover an application example (cart-pole problem simulation) shows the promising results of the proposed rule base reduction method. Keywordsreinforcement learning-fuzzy Q-learning-fuzzy rule interpolation-fuzzy rule base reduction
Reinforcement learning is a well known topic in computational intelligence. It can be used to solve control problems in unknown environments without defining an exact method on how to solve problems in various situations. Instead the goal is defined and all the actions done in the different states are given feedback, called reward or punishment (positive or negative reward). Based on these rewards the system can learn which action is considered the best in a given state. A method called Q-learning can be used for building up the state-action-value function. This method uses discrete states. With the application of fuzzy reasoning the method can be extended to be used in continuous environment, called Fuzzy Q-learning (FQ-Learning). Traditional Fuzzy Q-learning uses 0-order Takagi-Sugeno fuzzy inference. The main goal of this paper is to introduce Fuzzy Rule Interpolation (FRI), namely the FIVE (Fuzzy rule Interpolation based on Vague Environment) to be the model applied with Q-learning (FRIQ-learning). The paper also includes an application example: the well known cart pole (reversed pendulum) problem is used for demonstrating the applicability of the FIVE model in Q-learning.
Fuzzy Q-learning, the fuzzy extension of the Reinforcement Learning (RL) is a well known topic in computational intelligence. It can be used to tackle control problems in unknown continuous environments without defining an exact method on how to solve it explicitly. In the RL concept the problem needed to be solved is hidden in the feedback of the environment, called reward or punishment (positive or negative reward). From these rewards the system can learn which action is considered to be the best choice in a given state. One of the most frequently applied RL method is the "Q-learning". The goal of the Q-learning method is to find an optimal policy for the system by building the state-action-value function. The state-action-value-function is a function of the expected return (a function of the cumulative reinforcements), related to a given state and a taken action following the optimal policy. The original Q-learning method was introduced for discrete states and actions. With the application of fuzzy reasoning the method can be adapted for continuous environment, called Fuzzy Q-learning (FQ-Learning). Traditional Fuzzy Q-learning embeds the 0-order Takagi-Sugeno fuzzy inference and hence inherits the requirement of the state-action-value-function representation to be a complete fuzzy rule base. An extension of the traditional fuzzy Q-learning method with the capability of handling sparse fuzzy rule bases is already introduced by the authors, which suggests a Fuzzy Rule Interpolation (FRI) method, namely the FIVE (Fuzzy rule Interpolation based on Vague Environment) technique to be the reasoning method applied with Q-learning (FRIQ-learning). The main goal of this paper is the introduction of a method which can construct the requested FRI fuzzy model in a reduced size. The suggested reduction is achieved by incremental creation of an intentionally sparse fuzzy rule base.
Relatively few Fuzzy Rule Interpolation (FRI) techniques can be found among the practical fuzzy rule based applications. Many of them have limitations from the direct application point of view, for example they can be applied only in one dimensional case, or defined based on the two closest surrounding rules of the actual observation. Additionally the FRI methods can dramatically simplify the building of fuzzy rule bases by enabling the application of sparse rule bases. FRI methods can provide reasonable (interpolated) conclusions even if none of the existing rules fires under the current observation. These methods can help the expert to concentrate on the cardinal actions only. Compared to the classical fuzzy CRI, by omitting the derivable rules, the number of the fuzzy rules needed to be handled during the design process could be dramatically reduced. This paper provides a brief overview of several FRI methods and in more detailed an application oriented simple and quick FRI method FIVE will be introduced. For the demonstration of the benefits of the interpolation-based fuzzy reasoning as systematic approach, a robot navigation application is presented, where the robot is able to cycle through waypoints while avoiding collision with obstacles and walls. All the controlling parts were accomplished with fuzzy rule bases of the FIVE FRI method.
Several Fuzzy Rule Interpolation (FRI) techniques have limitations from the direct application point of view, for example their applicability is limited to the one dimensional case, or they can be defined only based on the two closest surrounding rules of the actual observation. This is the reason why relatively few FRI methods can be found among the practical fuzzy rule based applications. With the application of FRI methods sparse rule bases can be used, which substantially simplify the construction of fuzzy rule bases, because FRI methods can provide reasonable (interpolated) conclusions even if none of the existing rules fires under the gathered observation. Compared to the classical fuzzy CRI (compositional rule of inference), by eliminating the derivable rules, the number of the fuzzy rules needed in the rule base could be dramatically reduced. This paper provides a brief overview of several FRI methods and in more details an application oriented simple and quick FRI method "FIVE" will be introduced. For the demonstration of the benefits of the interpolation-based fuzzy reasoning as systematic approach, a robot guidance application is presented, where the robot is able to cycle through defined waypoints while avoiding collision with obstacles and walls. All of the controlling parts were accomplished with fuzzy rule bases of the "FIVE" FRI method.
From the viewpoint of Behaviour based Control many control tasks can be divided into separate behaviour components. By defining the relevant behaviour components, the actual control action can be constructed based on the individual control actions of the component behaviours. In this case the control action is either related to an individual behaviour component or to a fusion of behaviour components based on their relevance to the actual situation. This paper adapts the concept of fuzzy automaton for achieving the decision related to the relevance of the behaviour components in the task of the navigation of an autonomous vehicle. In the structure applied, the relevance of the behaviour components is approximated by a fuzzy rule interpolation (FRI, namely the FIVE method) based fuzzy automaton. The main reason for the FRI application is the state-transition rule-base simplification of the fuzzy automaton. In case of FRI, sparse rule bases (incomplete rule bases) are acceptable, because derivable rules can be omitted intentionally, saving construction time and reducing the complexity of the state-transition rule-base. The paper also provides a brief overview of Behaviour based Control and fuzzy rule interpolation (FRI). For demonstration purposes the paper gives a simple example of state-transition rule-base construction in case of the vehicle navigation task mentioned.