Coordination with an unknown human teammate is a notable challenge for cooperative agents. Behavior of human players in games with cooperating AI agents is often sub-optimal and inconsistent leading to choreographed and limited cooperative scenarios in games. This paper considers the difficulty of cooperating with a teammate whose goal and corresponding behavior change periodically. Previous work uses Bayesian models for updating beliefs about cooperating agents based on observations. We describe belief models for on-line planning, discuss tuning in the presence of noisy observations, and demonstrate empirically its effectiveness in coordinating with inconsistent agents in a simple domain. Further work in this area promises to lead to techniques for more interesting cooperative AI in games.

Interactive narrative systems produce branching story experiences for a human user using an interactive world. A class of interactive narrative systems, called strong story systems, manage a user's experience by manipulating the interactive world and its characters according to a formal story model. In these systems, a human user may place the world into a state such that the formal story model can no longer control interaction. One solution to this problem, called intervention, is to exchange the undesirable outcomes of a player's action for a set that do not violate the story model. However, the player may become aware that their intended action is being intervened against by a context-sensitive, meta-narrative process. In this paper we describe a method of ensuring game world alibis for interventions through domain modification of world mechanics.

The Markov Decision Process (MDP) framework is a versatile method for addressing single and multiagent sequential decision making problems. Many exact and approximate solution methods attempt to exploit structure in the problem and are based on value factorization. Especially multiagent settings (MAS), however, are known to suffer from an exponential increase in value component sizes as interactions become denser, meaning that approximation architectures are overly restricted in the problem sizes and types they can handle. We present an approach to mitigate this limitation for certain types of MASs, exploiting a property that can be thought of as "anonymous influence" in the factored MDP. In particular, we show how anonymity can lead to representational and computational efficiencies, both for general variable elimination in a factor graph but also for the approximate linear programming solution to factored MDPs. The latter allows to scale linear programming to factored MDPs that were previously unsolvable. Our results are shown for a disease control domain over a graph with 50 nodes that are each connected with up to 15 neighbors.

The topic of the self-confidence of autonomous systems is discussed from the perspective of its use in a military environment. The concepts of autonomy and self-confidence are quite different in a military environment from the civilian environment. The military’s recruit indoctrination provided a basis for the concept, the factors affecting the concept, and its measurement and communication. These and other aspects of the topic self-confidence in autonomous systems are discussed along with examples based on current research on the interface between human operators and such systems.

Culturally appropriate behavior is not genetically programmed, but is instead learned from direct teaching, or by The relevant literature maintains many different definitions observing and interacting with others. For example, language of culture, which vary according to the field of study. Hofstede is one of the primary abstract artifacts transmitted has studied the features that allow us to discern different extra genetically. This paper provides a review of how culturally cultures (Hofstede 2001), defining culture as: appropriate behavior can be achieved in synthetic agents and offers a concise overview of the relevant literature. "The collective programming of the mind that distinguishes the members of one group or category of people Bates (1994) describes believable characters as those from another" (Hofstede 2001, page 9).

Autonomous agents require interfaces to define their interactions with humans. The coupling between agents and humans is often limited, with disjoint goals between the agent interface and its associated autonomous components. This leads to a gap in human interaction relative to agent capabilities. We seek to aid interface designs by clarifying agent capabilities within an interface context. A taxonomy was developed that can help elucidate the agent’s affordances and constraints that guide interface design. Moreover, the descriptors employed in the taxonomy can serve as a common language to support dialog between agent and interface developers, resulting in improved autonomous systems that support human-autonomy coordination.

This study focuses on the question: "What are the computational formalisms at the heart of deceptive and counter-deceptive machines?" We formulate deception using a dynamic epistemic logic. Three different types of deception are considered: deception by lying, deception by bluffing and deception by truth-telling, depending on whether a speaker believes what he/she says or not. Next we consider various situations where an act of deceiving happens. Intentional deception is accompanied by a speaker's intent to deceive. Indirect deception happens when false information is carried over from person to person. Self-deception is an act of deceiving the self. We investigate formal properties of different sorts of deception.

Knowledge and its attendant phenomena are central to human storytelling and to the human experience more generally, but we find very few games that revolve around these concerns. This works to preclude a whole class of narrative experiences in games, and it also damages character believability. In this paper, we present an AI framework that supports gameplay with non-player characters who observe and form knowledge about the world, propagate knowledge to other characters, misremember and forget knowledge, and lie. We outline this framework through the lens of a gameplay experience that is intended to showcase it, called Talk of the Town, which we are currently developing. From a review of earlier projects, we find that our system has a novel combination of features found only independently across other systems, and that it is among the first to support character memory fallibility.

Intelligent systems rely on pattern recognition and signature-based approaches for a wide range of sensors enhancing situational awareness. For example, autonomous systems depend on environmental sensors to perform their tasks and secure systems depend on anomaly detection methods. The availability of large amount of data requires the processing of data in a “streaming” fashion with online algorithms. Yet, just as online learning can enhance adaptability to a non-stationary environment, it introduces vulnerabilities that can be manipulated by adversaries to achieve their goals while evading detection. Although human intelligence might have evolved from social interactions, machine intelligence has evolved as a human intelligence artifact and been kept isolated to avoid ethical dilemmas. As our adversaries become sophisticated, it might be time to revisit this question and examine how we can combine online learning and reasoning leading to the science of deceptive and counter-deceptive machines.

In order to use computational intelligence for automated narrative synthesis, domain knowledge of the story world must be defined, a task which is currently confined to experts. This paper discusses the benefits and tradeoffs between agent-centric and event-centric approaches towards authoring the domain knowledge of story worlds. In an effort to democratize story world creation, we present an accessible graphical platform for content creators and even end users to create their own story worlds, populate it with smart characters and objects, and define narrative events that can be used by existing tools for automated narrative synthesis. We demonstrate the potential of our system by authoring a simple bank robbery story world, and integrate it with existing solutions for event-centric planning to synthesize example digital stories.