In this paper, we examine the performance of fuzzy rule-based systems in a car racing domain. Fuzzy rule-based systems are used for high-level decision making of a car agent. We examine two methods that generate a set of training patterns for constructing fuzzy rule-based systems. We also examine the effect of sensory information on the high-level decision making. The performance of four types of fuzzy rule-based systems are compared in a series of computational experiments. The analysis of using different types of sensory information and different methods for generating training patterns is also performed.

In this paper, we examine the performance of fuzzy rule-based systems in a car racing domain. Fuzzy rule-based systems are used for high-level decision making of a car agent. We examine two methods that generate a set of training patterns for constructing fuzzy rule-based systems. We also examine the effect of sensory information on the high-level decision making. The performance of four types of fuzzy rule-based systems is compared in a series of computational experiments. The analysis of using different types of sensory information and different methods for generating training patterns is also performed.

The performance of fuzzy rule-based systems are examined in a car racing domain. In the car racing domain, two car agents compete for waypoints. Fuzzy rule-based systems are used to determine which waypoints the car agent should aim at. Training patterns for constructing fuzzy rule-based systems are generated through preliminary experiments against a fixed opponent car agent. We examine the effect of the opponent car agents on the performance of the fuzzy rule-based systems.

We examine the task of constructing bounded-time self-stabilizing rule-based systems that take their input from an external environment. Bounded response-time and self-stabilization are essential for rule-based programs that must be highly fault-tolerant and perform in a real-time environment. We present an approach for solving this problem using the OPS5 programming language as it is one of the most expressive and widely used rule-based programming languages. Bounded response-time of the program is ensured by constructing the state space graph so that the programmer can visualize the control flow of the program execution. Potential infinite firing sequences, if any, should be detected and the involved rules should be revised to ensure bounded termination. Both the input variables and internal variables are made fault-tolerant from corruption caused by transient faults via the introduction of new self-stabilizing rules in the program. Finally, the timing analysis of the self-stabilizing OPS5 program is shown in terms of the number of rule firings and the comparisons performed in the Rete network.

This paper examines the task of constructing bounded-time self-stabilizing rule-based systems that take their input from an external environment. Bounded response-time and self-stabilization are essential for rule-based programs that must be highly fault-tolerant and perform in a real-time environment. We present an approach for solving this problem using the OPS5 programming language as it is one of the most expressive and widely used rule-based programming languages. Bounded response-time of the program is ensured by constructing the state space graph so that the programmer can visualize the control flow of the program execution, and any possible infinite execution leaps should be detected. Both the input variables and internal variables are made fault tolerant from corruption caused by transient faults via the introduction of new self-stabilizing rules in the program. Finally the timing analysis of the self-stabilizing OPS5 program is shown in terms of the number of rule firings and the comparisons performed in the Rete network.