Behaviors that emerge from emotion and cognition: implementation and evaluation of a symbolic-connectionist architecture

This paper describes the implementation and evaluation of a framework for modeling emotions in complex, decision-making agents. Sponsored by U.S. Army Research Institute (ARI), the objective of this research is to make the decision-making process of complex agents less predictable and more realistic, by incorporating emotional factors that affect humans. In tune with modern theories of emotions, we regard emotions essentially as subconscious signals and evaluations that inform, modify, and receive feedback from a variety of sources including higher cognitive processes and the sensorimotor system. Thus, our work explicitly distinguishes the subconscious processes (in a connectionist implementation) and the decision making that is subject to emotional influences (in a symbolic cognitive architecture). It is our position that “emotional states ” are emergent patterns of interaction between decision-making knowledge and these emotional signal systems. To this end, we have adopted an approach that promotes the emergence of behavior as a result of complex interactions between factors affecting emotions, integrated in the connectionist-style model, and factors affecting decision making, represented in the symbolic model. This paper presents the implementation of emotions architecture and explains how we evaluated the system. This includes a description of the behaviors we used in our prototype, the design of our experiments, a representative set of behavior patterns that emerged as a result of exercising our model over the design space, and our project’s lessons learne

A Connectionist-Symbolic Approach To Modeling Agent Behavior: Neural Networks Grouped By Contexts

A recent report by the National Research Council (NRC) declares neural networks “hold the most promise for providing powerful learning models”. While some researchers have experimented with using neural networks to model battlefield behavior for Computer Generated Forces (CGF) systems used in distributed simulations, the NRC report indicates that further research is needed to develop a hybrid system that will integrate the newer neural network technology into the current rule-based paradigms. This paper supports this solicitation by examining the use of a context structure to modularly organize the application of neural networks to a low-level Semi-Automated Forces (SAF) reactive task. Specifically, it reports on the development of a neural network movement model and illustrates how its performance is improved through the use of the modular context paradigm. Further, this paper introduces the theory behind the neural networks’ architecture and training algorithms as well as the specifics of how the networks were developed for this investigation. Lastly, it illustrates how the networks were integrated with SAF software, defines the networks’ performance measures, presents the results of the scenarios considered in this investigation, and offers directions for future work

An architecture for emotional decision-making agents

Our research focuses on complex agents that are capable of interacting with their environments in ways that are increasingly similar to individual humans. In this article we describe a cognitive architecture for an interactive decision-making agent with emotions. The primary goal of this work is to make the decision-making process of complex agents more realistic with regard to the behavior moderators, including emotional factors that affect humans. Instead of uniform agents that rely entirely on a deterministic body of expertise to make their decisions, the decision making process of our agents will vary according to select emotional factors affecting the agent as well as the agent\u27s parameterized emotional profile. The premise of this model is that emotions serve as a kind of automatic assessment system that can guide or otherwise influence the more deliberative decision making process. The primary components of this emotional system are pleasure/pain and clarity/confusion subsystems that differentiate between positive and negative states. These, in turn, feed into an arousal system that interfaces with the decision-making system. We are testing our model using synthetic special-forces agents in a reconnaissance simulation

Lecture Notes in Computer Science 1 A Connectionist-Symbolic Approach to Modeling Agent Behavior: Neural Networks Grouped by Contexts

Abstract. A recent report by the National Research Council (NRC) declares neural networks “hold the most promise for providing powerful learning models”. While some researchers have experimented with using neural networks to model battlefield behavior for Computer Generated Forces (CGF) systems used in distributed simulations, the NRC report indicates that further research is needed to develop a hybrid system that will integrate the newer neural network technology into the current rule-based paradigms. This paper supports this solicitation by examining the use of a context structure to modularly organize the application of neural networks to a low-level Semi-Automated Forces (SAF) reactive task. Specifically, it reports on the development of a neural network movement model and illustrates how its performance is improved through the use of the modular context paradigm. Further, this paper introduces the theory behind the neural networks ’ architecture and training algorithms as well as the specifics of how the networks were developed for this investigation. Lastly, it illustrates how the networks were integrated with SAF software, defines the networks ’ performance measures, presents the results of the scenarios considered in this investigation, and offers directions for future work.

An Architecture for Emotional Decision-Making Agents

Our research focuses on complex agents that are capable of interacting with their environments in ways that are increasingly similar to individual humans. In this article we describe a cognitive architecture for an interactive decisionmaking agent with emotions. The primary goal of this work is to make the decision-making process of complex agents more realistic with regard to the behavior moderators, including emotional factors that affect humans. Instead of uniform agents that rely entirely on a deterministic body of expertise to make their decisions, the decision making process of our agents will vary according to select emotional factors affecting the agent as well as the agent's parameterized emotional profile. The premise of this model is that emotions serve as a kind of automatic assessment system that can guide or otherwise influence the more deliberative decision making process. The primary components of this emotional system are pleasure/pain and clarity/confusion subsystems that differentiate between positive and negative states. These, in turn, feed into an arousal system that interfaces with the decision-making system. We are testing our model using synthetic specialforces agents in a reconnaissance simulation

Modeling Semi-Automated Forces with Neural Networks: Performance Improvement through a Modular Approach

A recent report by the National Research Council (NRC) declares neural networks "hold the most promise for providing powerful learning models". While some researchers have experimented with using neural networks to model battlefield behavior for Computer Generated Forces (CGF) systems used in distributed simulations, the NRC report indicates that further research is needed to develop a hybrid system that will integrate the newer neural network technology into the current rule-based paradigms. This paper supports this solicitation by examining the use of a context structure to modularly organize the application of neural networks to a low-level Semi-Automated Forces (SAF) reactive task. Specifically, it reports on the development of a neural network movement model and illustrates how its performance is improved through the use of the modular context paradigm. Further, this paper introduces the theory behind the neural networks' architecture and training algorithms as well as the specifics of how the networks were developed for this investigation. Lastly, it illustrates how the networks were integrated with SAF software, defines the networks' performance measures, presents the results of the scenarios considered in this investigation, and offers directions for future work

A Symbolic-Connectionist Framework for Representing Emotions in Computer Generated Forces

In concert with the I/ITSEC ’01 theme, “Warfighting Readiness Through Innovative Training Technology”, this paper explores an innovative approach to enhancing the realism and hence the efficacy of training – developing the capacity for synthetic forces to act and respond emotionally. Emotions, along with moods and dispositions, have been shown to be important determiners of behaviors. They influence how situations are interpreted, how attention is focused, which actions are considered, and how these actions are executed. For example, individuals who are afraid will more readily interpret a situation as dangerous, have their focus of attention narrowed down to the source of their fear, and be biased toward actions that can reduce their level of fear. Similarly, individuals who are angry will more readily interpret others as being hostile, have their focus of attention narrowed down to the source of their anger, and be biased toward aggressive and/or retaliatory actions. Understanding and modeling variations in emotions will be crucial for producing realistic human-like behavior in synthetic forces. The Army has recognized this potential and is now emphasizing the need for such human behavioral characteristics as being vitally important to training. This paper discusses fundamental principles of emotions research and then applies these principle