A candidate framework for integration of theoretical, modeling and experimental approaches to understanding emotional intelligence is described. The framework includes three elements of a new kind that enable representation of emotional cognition: an emotional state, an appraisal, and a moral schema. These elements are integrated with the weak semantic cognitive map representing the values of emotional appraisals. The framework is tested on interpretation of results obtained in two new experimental paradigms that reveal general features of human emotional cognition, such as the emergence of subjectively perceived persistent roles of individual virtual actors. Implications concern heterogeneous human-robot teams.

The two common design principles for agent-based models, KISS (Keep It Simple, Stupid) and KIDS (Keep It Descriptive, Stupid) offer limited traction for developing cognitive agents, who typically have strong ties to research findings and established theories of cognition. A KIKS principle (Keep It Knowledgeable, Stupid) is proposed to capture the fact that cognitive agents are grounded in published research findings and theory, rather than simply selecting parameters in an ad-hoc way. In short, KIKS suggests that modelers should not focus on how many parameters, but should instead focus on choosing the right research papers and implement each of their key parameters and mechanisms. Based on this principle, a design process for creating cognitive agents based on cognitive models is proposed. This process is centered around steps that cognitive agent designers are already consider (e.g., literature search, validation, implementing a computational model). However, the KIKS process suggests two differences. First, KIKS calls for reporting explicit metadata on the empirical and theoretical relationships that an agent's cognitive model is intended to capture. Each such relationship should be associated with a published paper that supports it. This metadata would serve a valuable purpose for comprehending, validating, and comparing the cognitive models used by different agents. Second, KIKS calls for validation tests to be specified before creating an agent's cognitive model computationally. This process, known as test-driven design, can be used to monitor the adherence of a cognitive agent to its underlying knowledge base as it evolves through different versions. Implications, advantages, and limitations of the proposed process for KIKS are discussed.

In this article we describe a general cognitive model of human social behavior that is meant to increase the social intelligence of autonomous intelligent agents in different contexts. Despite the remarkable improvements that have been made on human-agent interaction, agents still have a limited capacity to be aware of the social reality that is present in the human mind and significantly guides human behavior. The model discussed in this paper is a step toward increasing that capacity significantly. Two different case studies are described in which the proposed model is used to better explain and predict human behavior. The first case study is the well known Ultimatum game. The second one is a variation of the “Game of Nines” played by children.

We present CLASS QL , a multi-agent model for playing real-time strategy games, where learning and control of our own team’s units is decentralized; each agent uses its own reinforcement learning process to learn and control units of the same class. Coordination between these agents occurs as a result of a common reward function shared by all agents and synergistic relations in a carefully crafted state and action model for each class. We present results of CLASS QL against the built-in AI in a variety of maps using the Wargus real-time strategy game.

This paper proposes a method for the development of AI for autonomous agents in game worlds modeled by fixed regular grids.  This approach uses context behaviors driven by distance maps to support multiple agents in a dynamic environment.  This paper introduces the notion of hierarchical distance maps which allow for higher-level goals to be easily specified by designers.  We also discuss potential applications of our approach in the design and development of agents and behaviors in the block world genre.

This article describes a technical approach to movement-based interactions between a human interactor and an intelligent agent based on the theatrical Viewpoints movement framework. The Viewpoints AI system features procedural gesture interpretation using shallow semantics and deep aesthetics analysis from the Viewpoints framework. The installation creates a liminal virtual / real space for the human and AI to interact by the use of digital projection for the AI visualization and shadow play to represent the human. Observations from a recent public demonstration of the system and future directions of work are also discussed.

In this paper we describe a method of modeling play styles as deviations from approximations of game theoretically rational actions. These deviations are interpreted as containing information about player skill and player decision making style. We hypothesize that this information is useful for differentiating between players and for understanding why human player behavior is attributed intentionality which we argue is a prerequisite for believability. To investigate these hypotheses we describe an experiment comparing 400 games in the Mario AI Benchmark testbed, played by humans, with equivalent games played by an approximately game theoretically rationally playing AI agent. The player actions’ deviations from the rational agent’s actions are subjected to feature extraction, and the resulting features are used to cluster play sessions into expressions of different play styles. We discuss how these styles differ, and how believable agent behavior might be approached by using these styles as an outset for a planning agent. Finally, we discuss the implications of making assumptions about rational game play and the problematic aspects of inferring player intentions from behavior.

We present a multi-layer hierarchical connectionist network model for simulating human autobiographical memory in believable agents. Grounded in psychological theory, this model improves on previous attempts to model agents’ event knowledge by providing a more dynamic and non-deterministic representation of autobiographical memories. From this model, a Java-based proof-of-concept prototype system was created for use as an enabling technology in video games. This prototype was leveraged in the creation of a Minecraft modification (mod) implementation of the model that is able to demonstrate context-dependent recall and the effects of recency on memory recall. Wider implications of the model in agent and game design are discussed.

This article describes a technical approach to movement-based interactions between a human interactor and an intelligent agent based on the theatrical Viewpoints movement framework. The Viewpoints AI system features procedural gesture interpretation using shallow semantics and deep aesthetics analysis from the Viewpoints framework. The installation creates a liminal virtual / real space for the human and AI to interact by the use of digital projection for the AI visualization and shadow play to represent the human. Observations from a recent public demonstration of the system and future directions of work are also discussed.

This paper describes the Intelligent Engine (IE) of ECHOES, a serious game built for helping young children with Autism Spectrum Conditions acquire social communication skills. ECHOES IE's main component is an autonomous virtual agent that acts as a credible social partner for children with autism by engaging them in interactive learning activities. The other IE components are a user model, a drama manager and a social communication engine. We discuss how AI technology allows us to satisfy the requirements for the design of the agent and the learning activities that we identified through consultations with children and carers and a review of best practices for autism intervention. We present experimental results pertaining to the agent's effectiveness, which show encouraging improvements for a number of children.