Type-2 Fuzzy Logic and Systems

This chapter describes the research from T2 FS-based MoGoTW system to DyNamic DarkForest (DyNaDF) open platform for human and machine co-learning on Go. A human Go player’s performance could be influenced by some factors, such as the on-the-spot environment as well as physical and mental situations of the day. In the first part, we used a sample of games played against machine to estimate the human’s strength (Lee et al. in IEEE Trans Fuzzy Syst 23(2):400–420, 2015 [1]). The Type-2 Fuzzy Sets (T2 FSs) with parameters optimized by a genetic algorithm for estimating the rank was presented (Lee et al. in IEEE Trans Fuzzy Syst 23(2):400–420, 2015 [1]). The T2 FS-based adaptive linguistic assessment system inferred the human performance and presented the results using the linguistic description (Lee et al. in IEEE Trans Fuzzy Syst 23(2):400–420, 2015 [1]). In March 2016, Google DeepMind challenge match between AlphaGo and Lee Sedol in Korea was a historic achievement for computer Go development. In Jan. 2017, an advanced version of AlphaGo, Master, won 60 games against some top professional Go players. In May 2017, AlphaGo defeated Ke Jie, the top professional Go player, at the Future of Go Summit in China. In second part, we showed the development of computational intelligence (CI) and its relative strength in comparison with human intelligence for the game of Go (Lee et al. in IEEE Comput Intell Mag 11(3):67–72, 2016 [2]). Additionally, we also presented a robotic prediction agent to infer the winning possibility based on the information generated by DarkForest Go engine and to compute the winning possibility based on the partial game situation inferred by FML assessment engine (Lee et al. in FML-based prediction agent and its application to game of Go, 2017 [3]). Moreover, we chose seven games from 60 games to evaluate the performance (Lee et al. in FML-based prediction agent and its application to game of Go, 2017 [3]). In this chapter, we extract the human domain knowledge from Master’s 60 games for giving the desired output. Then, we combine Particle Swarm Optimization (PSO) and FML to learn the knowledge base and further infer the game results of Google AlphaGo in May 2017. The experimental results show that the proposed approach is feasible for the application to human and machine co-learning on Go. In the future, powerful computer Go programs such as AlphaGo are expected to be instrumental in promoting Go education and AI real-world applications.

The novel weighted averages (NWAs) are extensions of the linear arithmetic weighted average and are powerful tools in aggregating diverse inputs including numbers, intervals, type-1 fuzzy sets (T1 FSs), words modeled by interval type-2 fuzzy sets, or a mixture of them. In contrast to the linear arithmetic weighted average, the ordered weighed average (OWA) is a nonlinear operator that can implement more flexible mappings, and hence it has been widely used in decision-making. In many situations, however, providing crisp numbers for either the sub-criteria or the weights is problematic (there could be uncertainties about them), and it is more meaningful to provide intervals, T1 FSs, words, or a mixture of all of these, for the sub-criteria and weights. Ordered NWAs are introduced in this chapter. They are also compared with NWAs and Zhou et al’s fuzzy extensions of the OWA. Examples show that generally the three aggregation operators give different results.

In a typical agricultural field operation, an agricultural vehicle must be accurately navigated to achieve an optimal result by covering with minimal overlap during tillage, fertilizing and spraying. To this end, a small scale tractor-trailer system is equipped by using off the shelf sensors and actuators to design a fully autonomous agricultural vehicle. To alleviate the task of the operator and allow him to concentrate on the quality of work performed, various systems were developed for driver assistance and semi-autonomous control. Real-time experiments show that a controller, which gives a satisfactory trajectory tracking performance for a straight line, gives a large steady-state error for a curved line trajectory. On the other hand, if the controller is aggressively tuned to decrease the tracking error for the curved lines, the controller gives oscillatory response for the straight lines. Although existing autonomous agricultural vehicles use conventional controllers, learning control algorithms are required to handle different trajectory types, environmental uncertainties, such as variable crop and soil conditions. Therefore, adaptability is a must rather than a choice in agricultural operations. In terms of complex mechatronics systems, e.g. an agricultural tractor-trailer system, the performance of model-based and model-free control, i.e. nonlinear model predictive control and type-2 neuro-fuzzy control, is compared and contrasted, and eventually some design guidelines are also suggested.

The author points out three important issues: (1) when should interval type-2 (IT2) fuzzy control be utilized, (2) how to design IT2 fuzzy controllers, and (3) how to analyze IT2 fuzzy controllers. Discussion is focused on application and practicality.

Type-2 fuzzy systems can be of great help in image analysis and pattern recognition applications. In particular, edge detection is a process usually applied to image sets before the training phase in recognition systems. This preprocessing step helps to extract the most important shapes in an image, ignoring the homogeneous regions and remarking the real objective to classify or recognize. Many traditional and fuzzy edge detectors can be used, but it is very difficult to demonstrate which one is better before the recognition results are obtained. In this work we show experimental results where several edge detectors were used to preprocess the same image sets. Each resulting image set was used as training data for a neural network recognition system, and the recognition rates were compared. In this paper we present the advantage of using a general type-2 fuzzy edge detector method in the preprocessing phase of a face recognition system. The Sobel and Prewitt edge detectors combined with GT2 FSs are considered in this work. In our approach, the main idea is to apply a general type-2 fuzzy edge detector on two image databases to reduce the size of the dataset to be processed in a face recognition system. The recognition rate is compared using different edge detectors including the fuzzy edge detectors (type-1, interval, and general type-2 FS) and the traditional Prewitt and Sobel operators.

In this chapter, we will present the novel applications of the Interval Type-2 (IT2) Fuzzy Logic Controllers (FLCs) into the research area of computer games. In this context, we will handle two popular computer games called Flappy Bird and Lunar Lander. From a control engineering point of view, the game Flappy Bird can be seen as a classical obstacle avoidance while Lunar Lander as a position control problem. Both games inherent high level of uncertainties and randomness which are the main challenges of the game for the player. Thus, these two games can be seen as challenging testbeds for benchmarking IT2-FLCs as they provide dynamic and competitive elements that are similar to real-world control engineering problems. As the game player can be considered as the main controller in a feedback loop, we will construct an intelligent control systems composed of three main subsystems: reference generator, the main controller, and game dynamics. In this chapter, we will design and then employ an IT2-FLC as the main controller in a feedback loop such that to have a satisfactory game performance while be able to handle the various uncertainties of the games. In this context, we will briefly present the general structure and the design methods of two IT2-FLCs which are the Single Input and the Double Input IT2-FLCs. We will show that the IT2-FLC structure is capable to handle the uncertainties caused by the nature of the games by presenting both simulations and real-time game results in comparison with its Type-1 and conventional counterparts. We believe that the presented design methodology and results will provide a bridge for a wider deployment of Type-2 fuzzy logic in the area of the computer games.

In the last two decades the intelligent agents have improved the lifestyle of human beings from different aspects of view such as life activities and services. Considering the importance of the safety and security role in the e-procurement, there have been many systems developed including trust engine. In particular, some of the first systems were modeled though trust evaluation concepts as crisp values, but now a days to adjust the systems with real world cases, the uncertainty and impreciseness parameters must be considered with the use of fuzzy sets theory. In this paper to minimize the number of exceptions related to suppliers, Trust Management Agent (TMA) is considered to prioritize candidate suppliers based on trust criteria. Due to lots of uncertainties, type-2 fuzzy sets prove to be a most suitable methodology to deal with the trust evaluation process efficiently. In this regard, a new evaluation process based on hierarchical Linguistic Weighted Averaging (LWA) sets is proposed. The solution method was then illustrated through a simple example which clarifies the suitability as well as the simplicity of the proposed method for the category of the defined problem.