Programming by demonstration is reaching industrial applications, which allows non-experts to teach new tasks without manual code writing. However, a certain level of complexity, such as online ...decision making or the definition of recovery behaviors, still requires experts that use conventional programming methods. Even though, experts cannot foresee all possible faults in a robotic application. To encounter this, we present a framework where user and robot collaboratively program a task that involves online decision making and recovery behaviors. Hereby, a task-graph is created that represents a production task and possible alternative behaviors. Nodes represent start, end or decision states and links define actions for execution. This graph can be incrementally extended by autonomous anomaly detection, which requests the user to add knowledge for a specific recovery action. Besides our proposed approach, we introduce two alternative approaches that manage recovery behavior programming and compare all approaches extensively in a user study involving 21 subjects. This study revealed the strength of our framework and analyzed how users act to add knowledge to the robot. Our findings proclaim to use a framework with a task-graph based knowledge representation and autonomous anomaly detection not only for initiating recovery actions but particularly to transfer those to a robot.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
In this paper, a novel method to solve Fully Fuzzy Mixed Integer Linear Programming (FFMILP) problems is presented. Our method is based on the definition of membership function and a fuzzy ...interactive technique for solving the classical multiobjective programming. It is worthwhile to note that this is the first time that the fully fuzzy mixed integer linear programming problem is discussed and a solving method is presented. To illustrate the steps of the proposed method, some numerical examples are solved and the results are compared with other methods in the literature. Computational results present the application of the method.
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•An administrative and market-based optimization method is presented for solving a regional water resources allocation problem.•A multi-objective bi-level programming model is proposed.•Multiple ...uncertainties are considered.•A bi-level interactive method based on satisfactory solution with global–local neighbor adaptive particle swarm optimization (GLN-aPSO) is designed.•The practicality and efficiency of the model is verified through an analysis of a real-life example.
The aim of this paper is to present an administrative and market-based optimization method for solving a problem of regional water resources allocation by considering a hierarchical structure under multiple uncertainties. To accomplish this, a multi-objective bi-level programming model is developed based on the water right distribution in a river basin. In this model, the stream flow (i.e., water supply) and water demand are considered as a fuzzy random variable and a random fuzzy variable, respectively. The regional authority, the leader in the hierarchy, seeks to maximize the total benefit to society while simultaneously minimizing pollution emissions. The sub-areas, the followers in the hierarchy, seek to maximize their own economic benefits. To deal with the inherent uncertainty, a transformation of variables into fuzzy variables is done, and through the expected value operation, the fuzzy variables are subsequently transformed into determined ones. For solving the complex non-linear bi-level programming model, a bi-level interactive method based on satisfactory solution with global–local–neighbor adaptive particle swarm optimization (GLN-aPSO) is designed as a combined solution method. A case study is presented to demonstrate the applicability and efficiency of this method. The interactive solutions associated with different minimal satisfactory degrees of the two objectives in the upper level have been generated. They can help the regional authority and the sub-areas to identify desired water allocation schemes according to their preferences and practical conditions, as well as facilitate in-depth analyses of tradeoffs between the objectives in the two levels. Finally, to verify that it is reasonable to use bi-level programming the results are compared with those of using single level programming.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
The interactive programming (IP) using aspiration levels is a well-known method applied to multi-criteria decision making under certainty (M-DMC). However, some essential analogies between M-DMC and ...scenario-based one-criterion decision making under uncertainty (1-DMU) have been recently revealed in the literature. These observations give the opportunity to adjust the IP to a totaly new issue. The goal of the paper is to create two novel procedures for uncertain problems on the basis of the IP ideas: the first one for pure strategy searching and the second for mixed strategy searching. In many ways, they allow a better consideration of the decision maker's preferences than classical decision rules. One of their significant advantages consists in analyzing particular scenarios sequentially. Another strong point is that the new procedures can be used by any kind of decision makers (optimists, moderate, pessimists). The new approaches may be helpful when solving problems under uncertainty with partially known probabilities. Both methods are illustrated in the paper on the basis of two fictitious decision problems concerning the choice of an optimal location and the optimization of the stock portfolio structure.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Powerful algebraic techniques have been developed for classical sequential computation. Many of them are based on regular expressions and the associated regular algebra. For parallel and interactive ...computation, extensions to handle 2-dimensional patterns are often required. Finite interactive systems, a 2-dimensional version of finite automata, may be used to recognize 2-dimensional languages. In this paper we present a blueprint for getting a formal representation of parallel, interactive programs and of their semantics. It is based on a recently introduced approach for getting regular expressions for 2-dimensional patterns, particularly using words of arbitrary shapes and powerful control mechanisms on composition. We extend the previously defined class of expressions n2RE with new control features, progressively increasing the expressive power of the formalism up to a level where a procedure for generating the words accepted by finite interactive systems may be obtained. Targeted applications come from the area of modelling, specification, analysis and verification of structured interactive programs via the associated scenario semantics.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
The paper presents an interactive programming approach to find the compromise optimal solution of the multi-level multi-objective linear programming problem. The solving process can be divided into ...analysis stage and decision-making stage. In the analysis stage, an evaluation function is constructed to express the difference between the objective and ideal values. In the decision-making stage, decision maker can compare the objective values with ideal values. If there has unsatisfied objective values, decision maker can make a concession of the satisfied values to improve the unsatisfied values. When the satisfactory degree of decision maker in upper levels have been mat, the problem of lower levels will be in solving. A characteristic of the proposed method comes from its continuous interaction with the decision maker. Finally, a numerical example is solved by this algorithm.
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This paper considers interactive decision making methods for random fuzzy two-level linear programming problems. Assuming that the decision makers concern about the probabilities that their own ...objective function values are smaller than or equal to certain target values, fuzzy goals of the decision makers for the probabilities are introduced. Then, the possibility-based probability model to maximize the degrees of possibility with respect to the attained probability is considered. Interactive fuzzy nonlinear programming to obtain a satisfactory solution for the decision maker at the upper level in consideration of the cooperative relation between decision makers is presented. An illustrative numerical example demonstrates the feasibility and efficiency of the proposed method.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
All music performances are generative to the extent that the actions of performers produce musical sounds, but in this article the authors focus on performative interaction with generative music in a ...more compositional sense. In particular they discuss how live coding of music involves the building and management of generative processes. They suggest that the human interaction with generative processes that occurs in live coding provides a unique perspective on the generative music landscape. Especially significant is the way in which generative algorithms are represented in code to best afford interaction and modification during performance. They also discuss the features of generative processes that make them more or less suitable for live coding performances. They situate live coding practice within historical and theoretical contexts and ground the discussion with regular reference to their experiences performing in the live coding duo aa-cell.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
► Cooperative random fuzzy two-level linear programming problems are considered. ► Fuzzy goals are introduced into the formulated two-level programming problems. ► New models are proposed through ...possibilistic and stochastic programming. ► Interactive fuzzy nonlinear programming to obtain a satisfactory solution is presented. ► An illustrative numerical example demonstrates the feasibility and efficiency of the proposed method.
This paper focuses on interactive decision making methods for random fuzzy two-level linear programming problems. Considering the probabilities that the decision makers’ objective function values are smaller than or equal to target variables, fuzzy goals of the decision makers are introduced. Using the fractile model to optimize the target variables under the condition that the degrees of possibility with respect to the attained probabilities are greater than or equal to certain permissible levels, the original random fuzzy two-level programming problems are reduced to deterministic ones. Interactive fuzzy nonlinear programming to obtain a satisfactory solution for the decision maker at the upper level in consideration of the cooperative relation between decision makers is presented. An illustrative numerical example demonstrates the feasibility and efficiency of the proposed method.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
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