The past years have seen increasing cooperation between psychology and computer science in the field of computational modeling of emotion. However, to realize its potential, the exchange between the two disciplines, as well as the intradisciplinary coordination, should be further improved. We make three proposals for how this could be achieved. The proposals refer to: 1) systematizing and classifying the assumptions of psychological emotion theories; 2) formalizing emotion theories in implementation-independent formal languages (set theory, agent logics); and 3) modeling emotions using general cognitive architectures (such as Soar and ACT-R), general agent architectures (such as the BDI architecture) or general-purpose affective agent architectures. These proposals share two overarching themes. The first is a proposal for modularization: deconstruct emotion theories into basic assumptions; modularize architectures. The second is a proposal for unification and standardization: Translate different emotion theories into a common informal conceptual system or a formal language, or implement them in a common architecture.

The tremendous advances in gaming technologies over the past decade have focused primarily on the physical realism of the game environment and game characters, and the complexity and performance of game simulations and networking. However, current games are still lacking in the affective realism of the game characters, and the social complexity and realism of their interactions. To achieve the next leap in the level of engagement and effectiveness, particularly in the arena of serious games, gaming research needs to focus on enhancing the social and affective complexity and realism of the game characters, their interaction, and the game narrative as a whole. To achieve these goals, games and game development tools will need to provide functionality to support the recognition of user and game character emotions, real-time adaptation and appropriate responses to these emotions, and more realistic expression of emotions in game characters and user avatars. To support these functionalities, the games will need to construct affective models of the players, and include computational models of emotion within the game characters. In this paper, we discuss these functionalities, and suggest a set of requirements for an affective game engine, capable of supporting the development of more affectively realistic, engaging, and effective games. The discussion is organized around the functional requirements and the computational tasks necessary to support them. We emphasize the importance of selecting appropriate semantic primitives, and discuss how existing methods and techniques in affective computing and computational affective modeling contribute to the development of affective game engines and game development tools.

Causal models have recently become popular in artificial intelligence. Accompanying this popularity is an extensive use of the term in many different contexts, representing varying techniques in different domains. the aim of this article is threefold. First, to define the term causal model and to give an overview of causal reasoning in AI, in its many guises; second, to illustrate the advantages and limitations of causal model based reasoning by comparing it with the more traditional rule-based reasoning in knowledgebased diagnosis; and finally, to present work I have done in constructing a causal model of a problem-solving system and using it to reason about the system behavior. This discussion will focus on the modeling formalism used to represent the system structure and behavior, the types of reasoning possible using this formalism, and some principles for construction of causal models for software. the modeling formalism uses a state transition network to represent the states the system undergoes and their causal relationships. Each state in the net is attached to an object which represents some object or data structure in the modeled system. the objects' attributes in the uninstantiated model contain constraint expressions representing each attribute's relationship to the attributes of the neighboring objects. the basic reasoning mechanism is bi-directional value propagation through these constraint expressions. This mechanism supports several types of reasoning that make possible the complex diagnosis required to understand the behavior of a problem-solving system. These include backward causal tracing, simulation, comparative reasoning to understand the differences between the behavior of several objects, and reasoning about classes of objects and events in the system.

A new component of a problem-solving system, called the diagnosis module (DM), that enables the system to reason about its own behavior is described. The aim of the diagnosis is to identify inappropriate control parameter settings or faulty hardware components as the causes of observed misbehavior. The problem-solving system being diagnosed is a distributed interpretation system, the distributed vehicle monitoring testbed (DVMT), which is based on a blackboard problem-solving architecture. The diagnosis module uses a causal model of the expected behavior of the DVMT to guide the diagnosis. Causal-model-based diagnosis is not new in AI. What is different is the application of this technique to the diagnosis of problem-solving system behavior. Problem-solving systems are characterized by the availability of the intermediate problem-solving state, the large amounts of data to process, and in some cases, the lack of absolute standards for behavior. New diagnostic techniques that exploit the availability of the intermediate problem-solving state and address the combinatorial problem arising from the large amount of data to analyze are described. A technique has also been developed, called comparative reasoning, for dealing with cases where no absolute standard for correct behavior is available. In such cases the diagnosis system selects its own "correct behavior criteria" from objects within the problem-solving system which did achieve some desired situation. The diagnosis module for the DVMT has been implemented and successfully identifies faults.