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Machine Understanding and Avoidance of Misunderstanding in Agent-Directed Simulation and in Emotional Intelligence

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Machine Understanding and Avoidance of Misunderstanding in Agent-directed Simulation and in Emotional Intelligence Tuncer Ören1, Mohammad Kazemifard2 and Levent Yilmaz3 1School of Electrical Eng. & Computer Science,University of Ottawa, 800 King Edward Ave.,Ottawa,ON,Canada 2Department of Computer Engineering, Razi University, Kermanshah, Iran 3Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, U.S.A. Keywords: Agents with Emotional Intelligence, Emotional Intelligence Simulation, Machine Understanding, Avoidance of Misunderstanding. Abstract: Simulation is being applied in many very important projects and often it is a vitally important infrastructure for them. Several types of computational intelligence techniques have been part of the abilities of simula- tion. An important aspect of intelligence is the ability to understand. Agent-directed simulation (ADS) is a comprehensive paradigm to cover all aspects of synergy of software agents and simulation and our approach is to develop agents with understanding abilities. After a brief review of ADS, our paradigms of machine understanding is presented. The article clearly indicates types of misunderstandings that might occur. Our research plans are to avoid some of the misunderstandings which could occur and especially to have self- attesting abilities in our applications to document which types of misunderstandings are avoided. 1 INTRODUCTION After this introduction, we start with a concise review of our view of agent directed simulation Simulation is being applied in many very important which provides a comprehensive framework to projects and often it is a vitally important infrastruc- consider all aspects of the synergy of software ture for them. Several types of computational intelli- agents and simulation. In section 3, we discuss our gence techniques have been part of the abilities of four paradigms for machine understanding. Section simulation (Ören, 1995); (Yilmaz and Ören, 2009). 4 covers a systemati-zation of most types of An important aspect of intelligence is the ability misunderstanding appli-cable to machine to understand. Our research on machine understand- understanding. Sections 5 covers the conclusions ing started with understanding of simulation pro- and some of our plans for future research. grams (Ören et al., 1990) and evolved to understand- ing software in general (Ören, 1992), then to understanding systems (Ören, 2000), to agents with 2 AGENT-DIRECTED ability to understand emotions (Kazemifard et al., SIMULATION 2009), and finally to machine understanding in emo- tional intelligence simulation (Kazemifard, et al., Agent-Directed Simulation (ADS) is a unifying and 2013). comprehensive framework that allows integration of Failure avoidance has been recently intro-duced agent and simulation technologies (Ören, 2000). to advanced simulation studies as a paradigm in Agents are often considered as model design addition to validation and veri-fication studies (Ören metaphors in the development of simulations. Yet, and Yilmaz 2009). this narrow view limits the potential of agents in This article is built on our previous work improving various other dimensions of simulation especially on machine understanding as applied to (Yilmaz and Ören, 2009). To this end, ADS is agents used in simulation and to misunderstanding. comprised of three distinct, yet related areas that can However, in this article, we provide three additional be grouped under two categories as follows: machine understanding paradigms in addition to our . Simulation for Agents (i.e., agent simulation) basic machine understanding paradigm. Ören T., Kazemifard M. and Yilmaz L.. 318 Machine Understanding and Avoidance of Misunderstanding in Agent-directed Simulation and in Emotional Intelligence. DOI: 10.5220/0004635003180327 In Proceedings of the 3rd International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH-2013), pages 318-327 ISBN: 978-989-8565-69-3 Copyright c 2013 SCITEPRESS (Science and Technology Publications, Lda.) MachineUnderstandingandAvoidanceofMisunderstandinginAgent-directedSimulationandinEmotionalIntelligence involves the use of simulation modeling method- 3 MACHINE methodology and technologies to analyze, design, model, simulate, and test agent systems. This UNDERSTANDING includes, but is not limited to using agents as model design elements (i.e., agent-based In the study of natural phenomena, the role of simu- modeling). lation is often cited as “to gain insight” which is . Agents for Simulation: (1) Agent-supported another way of expressing “to under-stand.” Under- standing is one of the important philosophical topics. simulation involves the use of agents as support facilities to enable computer assistance in From a pragmatic point of view, it has a broad appli- simulation-based problem solving (e.g., simulation cation potential in many computerized studies in- cluding program understanding, machine vision, experiment management); (2) Agent-based simulation, on the other hand, focuses on the use fault detection based on machine vision as well as of agents for the generation of model behavior situation awareness and assessment. Therefore, sys- tematic studies of the elements, structures, archi- (e.g., simulator coordination, run-time models) in a simulation study as well as agent-initiated tectures, and scope of applications of com-puterized simulation. understanding systems as well as the characteristics of the results (or products) of understanding pro- In agent simulation, agents possess high-level cesses are warranted. mechanisms that include communication protocols Dictionary definitions of “to understand” include for interaction, task allocation, coordination of the following: to seize the meaning of, to accept as a actions, and conflict resolution at varying levels of fact, to believe, to be thoroughly acquainted with, to sophistication. Agent-based simulation focuses on form a reasoned judgment concerning something, to the use of agent technology to monitor and generate have the power of seizing meanings, forming rea- model behavior. This is similar to the use of soned judgments, to appreciate and sympathize with, Artificial Intelligence techniques for the generation to tolerate, and to possess a passive knowledge of a of model behavior (e.g., qualitative simulation and language. knowledge-based simulation). Agents can provide For machine understanding, or computerized un- cognitive architectures that allow reasoning and derstanding, we aim a limited scope as was ex- planning and serve as run-time models of simulation pressed in a previous publication: “We say that a model behavior management such as dynamic model system ‘knows about’ a class of objects, or relations, updating and symbiotic simulation. That is, context- if it has an internal relation for the class which ena- awareness of intelligent agents can facilitate bles it to operate on objects in this class and to simulator coordination, where runtime decisions for communicate with others about such operations. model staging and updating takes place to facilitate Thus, if a system knows about X, a class of objects dynamic composability. On the other hand, agent- or relations on objects, it is able to use an (internal) supported simulation enables the use of agents to representation of the class in at least the following support simulations as well as simulation studies by ways: receive information about the class, generate enhancing cognitive capabilities in problem elements in the class, recognize members of the specification, simulation experiment management, class and discriminate them from other class mem- and behavior analysis. bers, answer questions about the class, and take into Often, agent-supported simulation is used for the account information about changes in the class following purposes (Yilmaz and Ören, 2009): members” (Zeigler 1986)." (Ören, 2000). For addi- . To provide computer assistance for frontend and/or tional clarification of understanding and its philo- backend interface functions; sophical roots see Ören (2000). to process elements of a simulation study symbolically (for example, for consistency checks 3.1 Machine Understanding: and built-in reliability); and Basic Paradigm . to provide cognitive abilities to the elements of a simulation study, such as learning or As seen in Figure 1, an understanding system re- understanding abilities. quires the provision of a meta-model, the perception of the source in terms of the elements and constrains depicted in the meta-model, and an analyser that allows mapping of the perceived elements to con- structs of the meta-model. In this context, a model is an abstraction of phenomena or system, whereas a 319 SIMULTECH2013-3rdInternationalConferenceonSimulationandModelingMethodologies,Technologiesand Applications meta-model provides an abstraction of the properties of the model itself. A model conforms to its meta- B: entity to be understood model in the way that a computer program conforms to the grammar of the programming language in Understanding system A which it is developed. Analyzer D: perception of B (by A) Entity B with respect to C can understand C: meta-model of Bs Evaluator (interprets the relationship Cs: System A Ds: percep- (meta-knowledge meta-models tions about Bs) between C & D) 1 2 3 U: an understanding of B by A Relationships: C(s)-D(s) with respect to C Figure 1: Machine understanding – Basic paradigm. Figure 2: Functional decomposition of an under-standing
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