Emilio Vivancos

Parametric Timing Analysis

Embedded systems often have real-time constraints. Traditional timing analysis statically determines the maximum execution time of a task or a program in a real-time system. These systems typically depend on the worst-case execution time of tasks in order to make static scheduling decisions so that tasks can meet their deadlines. Static determination of worst-case execution times imposes numerous restrictions on real-time programs, which include that the maximum number of iterations of each loop must be known statically. These restrictions can significantly limit the class of programs that would be suitable for a real-time embedded system. This paper describes work-in-progress that uses static timing analysis to aid in making dynamic scheduling decisions. For instance, different algorithms with varying levels of accuracy may be selected based on the algorithms predicted worst-case execution time and the time allotted for the task. We represent the worst-case execution time of a function or a loop as a formula, where the unknown values affecting the execution time are parameterized. This parametric timing analysis produces formulas that can then be quickly evaluated at run-time so dynamic scheduling decisions can be made with little overhead. Benefits of this work include expanding the class of applications that can be used in a real-time system, improving the accuracy of dynamic scheduling decisions, and more effective utilization of system resources.

Parametric timing analysis and its application to dynamic voltage scaling

Embedded systems with real-time constraints depend on a priori knowledge of worst-case execution times (WCETs) to determine if tasks meet deadlines. Static timing analysis derives bounds on WCETs but requires statically known loop bounds. This work removes the constraint on known loop bounds through parametric analysis expressing WCETs as functions. Tighter WCETs are dynamically discovered to exploit slack by dynamic voltage scaling (DVS) saving 60% to 82% energy over DVS-oblivious techniques and showing savings close to more costly dynamic-priority DVS algorithms. Overall, parametric analysis expands the class of real-time applications to programs with loop-invariant dynamic loop bounds while retaining tight WCET bounds.

An Open Architecture for Affective Traits in a BDI Agent

Recently an increasing amount of research focuses on improving agents believability by adding affective features to the traditional agent-based modeling. This is probably due to the demand of reaching ever more realistic behaviors on agent-based simulations which extends to several and diverse application fields. The present work proposes O3A: an Open Affective Agent Architecture, which extends a traditional BDI agent architecture improving a practical reasoning with more “human” characteristics. This architecture tries to address disperse definitions combining the main elements of supporting psychological and neurological theories.

Integrating jason in a multi-agent platform with support for interaction protocols.

Agent communication is a core issue when studying all possible ways for agents to organize and collaborate to achieve their goals. We can count on communication standards, as the FIPA Interaction Protocols. On the other hand we can count on high level agent programming languages, like AgentSpeak, which allow us to model and represent the agent and its knowledge and behavior. In this paper we present a proposal to add to Jason (an interpreter of an extended version of AgentSpeak) a new level of abstraction in the task of programming conversations between agents. The agent communication follows the FIPA interaction protocols. A new entity called Communicator Manager acts as an interface between the agent programming language (Jason) and the platform communication facilities (Magentix 2). This approach allows the programmer to focus on programming the agent knowledge and reasoning parts instead of the interaction protocols. An agent can call the communication manager to start a conversation. The communication manager will control the different steps of the conversation and will modify the agent belief base to represent the results of the different steps of the conversation protocol. Therefore, the agents can use this knowledge in its reasoning process. This approach can be easily transfered to others agent programming languages and platforms.

Toward Formal Modeling of Affective Agents in a BDI Architecture

Affective characteristics are crucial factors that influence human behavior, and often, the prevalence of either emotions or reason varies on each individual. We aim to facilitate the development of agents’ reasoning considering their affective characteristics. We first identify core processes in an affective BDI agent, and we integrate them into an affective agent architecture (GenIA3). These tasks include the extension of the BDI agent reasoning cycle to be compliant with the architecture, the extension of the agent language (Jason) to support affect-based reasoning, and the adjustment of the equilibrium between the agent’s affective and rational sides.

Modeling Personality in the Affective Agent Architecture GenIA3.

In the last few years there has been a growing interest in affective computing. This type of computation tries to include and use emotions in different software processes. One of the most relevant areas is the simulation of human behavior where various affective models are used to represent different affective characteristics such as emotions, mood, or personality. Personality is defined as a set of individual characteristics that influence motivations and behaviors when a human being faces a particular circumstance. Personality plays a very important role in modeling affective processes. Through the simulation of emotions we can improve, among others, the experience of users dealing with machines, and human simulations in decision-making processes using multi-agent systems. In this work we propose a model for the use of personality in the general purpose architecture for affective agents GenIA3, as well as the development of the model in the current GenIA3

Integrating Expectations into Jason for Appraisal in Emotion Modeling.

Emotions have a strong influence on human reasoning and behavior, thus, in order to build intelligent agents which simulate human behavior, it is necessary to consider emotions. Expectations are one of the bases for emotion generation through the appraisal process. In this work we have extended the Jason agent language and platform for handling expectations. Unlike other approaches focused on expectations handling, we have modified the agent reasoning cycle to manage expectations, avoiding complex additional mechanisms such as monitors. This tool is part of the GenIA3architecture and, hence, is a step towards the standardization of the emotion modeling process in BDI (Beliefs-Desires-Intentions) agents.

Comparing the Execution Time of the Rete and Arlips2 Pattern Matching Algorithms.

En este articulo se demuestra que ni el lenguaje de desarrollo de sistemas basados en reglas Clips ni su algoritmo de pattern matching (Rete) son apropiados para ser empleados en entornos de inteligencia artificial en tiempo real. Proponemos las condiciones necesarias que debe cumplir un lenguaje basado en reglas para ser utilizado en entornos de tiempo real e implementamos un lenguaje que las cumple: Arlips2. El algoritmo de pattern matching de Arlips2 mantiene las buenas propiedades temporales de Rete y ademas cumple las condiciones necesarias para ser incorporado en un entorno de tiempo real. Ademas, las pruebas realizadas demuestran que mejora sustancialmente el tiempo de ejecucion de Rete en Clips 6.23.