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Problem Solving by Intelligent Search
Published in Konar Amit, Artificial Intelligence and Soft Computing, 2018
We have already come across some of the problems that can be solved by intelligent search. For instance, the well-known water-jug problem, the number puzzle problem and the missionaries-cannibals problem are ideal examples of problems that can be solved by intelligent search. Common experience reveals that a search problem is associated with two important issues: first ‘what to search’ and secondly ‘where to search’. The first one is generally referred to as ‘the key’, while the second one is termed ‘search space’. In AI the search space is generally referred to as a collection of states and is thus called state space. Unlike common search space, the state space in most of the problems in AI is not completely known, prior to solving the problem. So, solving a problem in AI calls for two phases: the generation of the space of states and the searching of the desired problem state in that space. Further, since the whole state space for a problem is quite large, generation of the whole space prior to search may cause a significant blockage of storage, leaving a little for the search part. To overcome this problem, the state space is expanded in steps and the desired state, called “the goal”, is searched after each incremental expansion of the state space.
Non-exponential Distributions in Reliability Modeling of PMS: Approximation and Simulation Approaches
Published in Mangey Ram, Modeling and Simulation Based Analysis in Reliability Engineering, 2018
Xiang-Yu Li, Jun Hu, Hong-Zhong Huang, Yan-Feng Li
This chapter presents two approaches of the evaluation methods to the non-exponential dynamic system and their use in the reliability analysis of the PMS. Traditionally, the commonly seen approaches of the PMS analysis methods are the combinatorial methods or the state-space model. The combinatorial methods are computationally efficient but not available in the dynamic system. The state-space-based methods can deal with various dynamic systems but suffer from the state explosion problem. The modular method is much better and combines the advantages of both methods. On the other hand, the non-exponential distribution is more practical to describe the lifetime of the components, but the traditional Markov model is not capable of doing so. Therefore, the use of the SMP or the MRGP that belong to the Markov renewal theory is shown in this chapter to deal with this problem. In this chapter, the reliability of the PMS with partially repairable components is evaluated in detail. Furthermore, the PMSs subject to random shocks is also discussed.
Progressive Modeling of 3D Building Rooftops from Airborne LiDAR and Imagery
Published in Jie Shan, Charles K. Toth, Topographic Laser Ranging and Scanning, 2018
MCMC, first introduced by Metropolis et al. (1953), is a method for obtaining a sequence of random samples from a probability distribution for which direct sampling is difficult. As the name suggests, MCMC is a combination of concepts of Monte Carlo sampling and Markov Chain. The Monte Carlo sampling is a method to generate a set of samples from a target density to compute integrals. Markov Chain refers to a sequence of random variables generated by a Markov process whose transition probabilities between different values in the state space depend only on the random variable’s current state, P(xt│xt–1,…,x0) = P(xt│xt–1). By combining these two concepts, the MCMC sampler can effectively explore a configuration space and approximates a target density.
A Supervised Model of Multivariable Control in Quadruple Tank System
Published in Applied Artificial Intelligence, 2023
Aravindan M, Chilambuchelvan A, Tamilselvi S
Fuzzy systems’ inferences need thorough knowledge about the QTP system input and output range for construction of accurate model. Moreover, in differential equation modeling, many assumptions are considered before modeling QTP system. If the assumptions were made incorrect, then entire modeling performance is inaccurate. In transfer function model, certain QTP system parameters need to be omitted and some assumptions are to be included, which is hard to perform the transfer function model with more accurate. For defining an operating point over the entire region of operation and obtaining linearization in that operating point is difficult in transfer function model based QTP system and lead to time complexity. Transfer function-based QTP system can be applied to linear time invariant systems, which is never suitable for highly non-linear QTP systems, non-linear arises due to interaction between two control loops. Moreover, major drawbacks of state space methods are state explosion problems. State space model-based construction of QTP system leads to time and space complexity. Machine Learning suits for modeling QTP systems with non-linear characteristics, unstable processes with Right Hand Side zero ([Nguyen and Le 2019; Sapitang et al. 2020; Wang and Wang 2020]).
SysML-based compositional verification and safety analysis for safety-critical cyber-physical systems
Published in Connection Science, 2022
Jian Xie, Wenan Tan, Zhibin Yang, Shuming Li, Linquan Xing, Zhiqiu Huang
As mentioned above, when designing and verifying complex systems, the divide-and-conquer method is always adopted. In other words, the complex system is decomposed into several subsystems. Accordingly, the contract of the complex system is decomposed into contracts related to the subsystems. Then, the subsystems and their contracts can be further decomposed. With this hierarchical design, the complexity of the management system can be abstracted. In the perspective of verification, the traditional verification method is to directly transform and verify the entire flat model. However, such a method often faces the problem of state space explosion when verifying the model of complex systems, which makes it difficult to perform formal verification of complex models.
Control-based event-driven bandwidth allocation scheme for video-surveillance systems
Published in Cyber-Physical Systems, 2022
Gautham Nayak Seetanadi, Karl-Erik Årzen, Martina Maggio
Model checking tools construct the whole state-space by building states and transitions, and storing it in memory. These tools have an upper limit on memory usage to limit runoff programs, restricting the size and complexity of system models. Classical model checking builds the whole state-space irrespective of the presence (or absence) of probabilities in transitions. Probabilistic models capture complex behaviours of the system but are larger in size in comparison to deterministic models. This state-space explosion causes memory issues during verification of complex system models.