Dynamically Scaled Fuzzy Control of Autonomous Intelligent Actor

The article presents an approach toward the implementation of an Autonomous Intelligent Actor’s (AIA) [1] fuzzy control mechanism, when each step of it is based on dynamically defined scale. Such a scale is directed by fuzzy conditional inference rule. The approach, offered in the article, allows “soft landing” of AIA on a Target even in a case of “unfriendly” docking situation.

A Fuzzy Logic Based Resolution Principal for Approximate Reasoning

In this article, we present a systemic approach toward a fuzzy logic based formalization of an approximate reasoning methodology in a fuzzy resolution, where we derive a truth value of A from both values of B → A and B by some mechanism. For this purpose, we utilize a t-norm fuzzy logic, in which an implication operator is a root of both graduated conjunction and disjunction operators. Furthermore by using an inverse approximate reasoning, we conclude the truth value of A from both values of B → A and B, applying an altogether different mechanism. A current research is utilizing an approximate reasoning methodology, which is based on a similarity relation for a fuzzification, while similarity measure is utilized in fuzzy inference mechanism. This approach is applied to both generalized modus-ponens/modus-tollens syllogisms and is well-illustrated with artificial examples.

A t-Norm Fuzzy Logic for Approximate Reasoning

A t-norm fuzzy logic is presented, in which a triangular norm (t-norm) plays the role of a graduated conjunction operator. Based on this fuzzy logic we develop methods for fuzzy reasoning in which antecedents and consequents involve fuzzy conditional propositions of the form “If x is A then y is B”, with A and B being fuzzy concepts (fuzzy sets). In this study, we present a systemic approach toward fuzzy logic formalization for approximate reasoning. We examine statistical characteristics of the proposed fuzzy logic. As the matter of practical interest, we construct a set of fuzzy conditional inference rules on the basis of the proposed fuzzy logic. Important features of these rules are investigated.

Some Considerations about Fuzzy Logic Based Decision Making by Autonomous Intelligent Actor

The article presents an approach toward an implementation of a fuzzy logic-based decision-making process by Autonomous Intelligent Actor (AIA) (© A. Tserkovny), when an input information is defined for its “strategic targeting” by a human operator in terms of a fuzzy incident geometry, whereas its “tactical” behavior (a navigation in space) is directed by fuzzy conditional inference rules. For implementing both elements of AIA decision-making a fuzzy logic [1] for formal geometric reasoning with extended objects is used. This fuzzy logic based fuzzification of axioms of an incidence geometry and a predicate apparatus [2] for AIA space orientation are also presented. The approach, offered in the article, extends predicates of a counter positioning of two objects and their mutual navigation into their fuzzy counterparts. The latter allows AIA to make certain “tactical” decisions.