http://sedici.unlp.edu.ar:80/handle/10915/134630 Thu, 12 Nov 2026 13:09:22 GMT 2026-11-12T13:09:22Z http://sedici.unlp.edu.ar:80/handle/10915/135307 Articulo Electronic Journal of SADIO; vol. 4 Intelligent agents and multi-agent systems (MAS) are one of the most rapidly developing areas of research of the last years. Agents offer new ways to analyze, design and implement software systems, improving, potentially, the ways in which software is modeled and then materialized by using a programming language Wed, 26 Jun 2002 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/135307 2002-06-26T00:00:00Z Intelligent agents and multi-agent systems (MAS) are one of the most rapidly developing areas of research of the last years. Agents offer new ways to analyze, design and implement software systems, improving, potentially, the ways in which software is modeled and then materialized by using a programming language http://sedici.unlp.edu.ar:80/handle/10915/135306 Articulo Electronic Journal of SADIO; vol. 4 Shortest route relaxation (SRR) of the Set Covering Problem (S C P ) is away of relaxing the problem to nd quick lower boundson the value of the objective function. A number of useful cost allocation strategies for the SRR are introduced which are applied to a number of test problems and computational results are presented. Wed, 26 Jun 2002 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/135306 2002-06-26T00:00:00Z Shortest route relaxation (SRR) of the Set Covering Problem (S C P ) is away of relaxing the problem to nd quick lower boundson the value of the objective function. A number of useful cost allocation strategies for the SRR are introduced which are applied to a number of test problems and computational results are presented. http://sedici.unlp.edu.ar:80/handle/10915/135304 Articulo Electronic Journal of SADIO; vol. 4 We present in this paper a secure deterministic cipher and decipher mechanism based on the well known Lorenz's dynamic system. The cipher process is performed by the combination of the message to be cipher and the states of the Lorenz's dynamic system, which act as the cipher key. The decipher process is carried out by the reconstruction of the key, which is generated using a Lorenz's system state observer. The observed key is then used in the decipher process in order to recover the ciphered message. Wed, 26 Jun 2002 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/135304 2002-06-26T00:00:00Z We present in this paper a secure deterministic cipher and decipher mechanism based on the well known Lorenz's dynamic system. The cipher process is performed by the combination of the message to be cipher and the states of the Lorenz's dynamic system, which act as the cipher key. The decipher process is carried out by the reconstruction of the key, which is generated using a Lorenz's system state observer. The observed key is then used in the decipher process in order to recover the ciphered message. http://sedici.unlp.edu.ar:80/handle/10915/135302 Articulo Electronic Journal of SADIO; vol. 4; https://sedici.unlp.edu.ar/handle/10915/2221 At the present, software developers are confronted to constant changes in the market. Due to this, they need obtaining flexible and customizable components to attend the changing nature of the applications. Using this kind ofcomponents in application construction can result expensive even with proper documentation. The documentation quality is basically important in the evolution phase. In reusable components documentation, most complex problems appear due to the abstract constitution of the design. Wed, 26 Jun 2002 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/135302 2002-06-26T00:00:00Z At the present, software developers are confronted to constant changes in the market. Due to this, they need obtaining flexible and customizable components to attend the changing nature of the applications. Using this kind ofcomponents in application construction can result expensive even with proper documentation. The documentation quality is basically important in the evolution phase. In reusable components documentation, most complex problems appear due to the abstract constitution of the design. http://sedici.unlp.edu.ar:80/handle/10915/135299 Articulo Electronic Journal of SADIO; vol. 4 We have studied the Reinforcement Function Design Process in two steps. For the first one we have considered the translation of a natural language description into an instance of our proposed Reinforcement Function General Expression. For the second step, we have gone deeply into the tuning of the parameters in this expression. It allowed us to obtain optimal definitions of the reinforcement function (relative to exploration). Since the General Expression is based on constraints, we have indentified them according to the type of state variable estimator on which they act, in particular: position and velocity.Using a particular, but representative Reinforcement Function (RF) expression, we study the relation between the Sum of each reinforcement type and the RF parameters during the exploration phase of the learning. For linear relations, we propose an analytic method to obtain the RF parameters values (no experimentation requires). For non-linear, but monotonous relations, we propose the Update Parameter Algorithm (UPA) and show that UPA can efficiently adjust the proportion of negative and positive reinforcements received during the exploratory phase of the learning. Additionally, we study the feasibility and consequences of adapting the RF during the learning process so as to improve the learning convergence of the system. Dynamic-UPA allows the whole learning process to maintain a desired ratio of positive and negative rewards. Thus, we introduce an approach to undertake the exploration-exploitation dilemma - a necessary step for efficient Reinforcement Learning. We show, with several experiments involving robots (mobile and arm), the performance of the proposed design methods. Finally, we emphasize the main conclusions and present some future directions of research. Tue, 01 Jan 2002 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/135299 2002-01-01T00:00:00Z We have studied the Reinforcement Function Design Process in two steps. For the first one we have considered the translation of a natural language description into an instance of our proposed Reinforcement Function General Expression. For the second step, we have gone deeply into the tuning of the parameters in this expression. It allowed us to obtain optimal definitions of the reinforcement function (relative to exploration). Since the General Expression is based on constraints, we have indentified them according to the type of state variable estimator on which they act, in particular: position and velocity.Using a particular, but representative Reinforcement Function (RF) expression, we study the relation between the Sum of each reinforcement type and the RF parameters during the exploration phase of the learning. For linear relations, we propose an analytic method to obtain the RF parameters values (no experimentation requires). For non-linear, but monotonous relations, we propose the Update Parameter Algorithm (UPA) and show that UPA can efficiently adjust the proportion of negative and positive reinforcements received during the exploratory phase of the learning. Additionally, we study the feasibility and consequences of adapting the RF during the learning process so as to improve the learning convergence of the system. Dynamic-UPA allows the whole learning process to maintain a desired ratio of positive and negative rewards. Thus, we introduce an approach to undertake the exploration-exploitation dilemma - a necessary step for efficient Reinforcement Learning. We show, with several experiments involving robots (mobile and arm), the performance of the proposed design methods. Finally, we emphasize the main conclusions and present some future directions of research.