Natural Language Understanding (NLU) studies machine language comprehension and action without human intervention. We describe an implemented system that supports deep semantic NLU for controlling systems with multiple simulated robot agents. The system supports bidirectional communication for both human-agent and agent-agent inter-action. This interaction is achieved with the use of N-tuples, a novel form of Agent Communication Language using shared protocols with content expressing actions or intentions. The system’s portability and flexibility is facilitated by its division into unchanging “core” and “application-specific” components.

This work presents a general-purpose character reasoning model intended for usage by autonomous team actors that are acting as believable characters (e.g., human team actors fall into this category). The idea is that selecting a cast of believable characters can predetermine a solution to an unexpected challenge that the team may be facing in a rescue mission. This approach in certain cases proves more efficient than an alternative approach based on rational decision making and planning, which ignores the question of character believability. This point is illustrated with a simple numerical example in a virtual world paradigm.

Narratives are powerful because of their impact on our emotional experiences. Recent years have witnessed significant advances in affective computing and intelligent interaction, presenting a broad range of opportunities for enhancing the design, implementation, and adaptivity of interactive narratives. This paper presents preliminary work examining story-centric games and interactive narratives from the perspective of psychological theories of emotion, with a particular focus on player affect. We examine the sources and duration of player emotion, social facets of emotion, players’ individual differences in emotion, and meta-emotions. Recommendations and future directions for research on player emotion in interactive narratives are discussed.

Multi-robot systems are an exciting application domain for AI research and Dec-POMDPs, specifically. MacDec-POMDP methods can produce high-quality general solutions for realistic heterogeneous multi-robot coordination problems by automatically generating control and communication policies, given a model. In contrast to most existing multi-robot methods that are specialized to a particular problem class, our approach can synthesize policies that exploit any opportunities for coordination that are present in the problem, while balancing uncertainty, sensor information, and information about other agents.

We have constructed a system that supports unscripted social interaction between a player and virtual characters, where the participants pursue internal agendas and respond to one another in real-time.  Our emphasis on unscripted interaction means that the characters must accept dynamically generated performance requests, while our concern with social interaction implies that the characters must interleave performances with an attention to natural flow that encourages social engagement. We present initial work on a performance management mechanism that produces this interleaving.   It initiates and suspends character performances by allocating animation resources to requests via a utility function representing aesthetic concerns.  That function weighs extrinsic factors reflecting the purpose of taking an action against intrinsic ones that concern features of a given performance.  We show, via multiple short videos, that the features are individually material to the aesthetic quality of the result and that the mechanism can produce aesthetically pleasing performances on par with the best hand-generated prioritization scheme. We argue, anecdotally, that the parameters of the model are easy to identify, suggesting that the feature vocabulary is both intuitive and useful for shaping character performances.

The number of applications of service robotics in public spaces such as hospitals, museums and malls is a growing trend. Public spaces, however, provide several challenges to the robot, and specifically with its planning capabilities: they need to cope with a dynamic and uncertain environment and are subject to particular human-robot interaction constraints. A major challenge is the Joint Intention problem. When cooperating with humans, a persistent commitment to achieve a shared goal cannot be always assumed, since they have an unpredictable behavior and may be distracted in environments as dynamic and uncertain as public spaces, and even more so if the human agents are customers,visitors or bystanders. In order to address such issues in a decision-making context, we present a framework based on Hierarchical Factored POMDPs. We describe the general method for ensuring the Joint Intention between human and robot , the hierarchical structure and the Value Decomposition method adopted to build it.We also provide an example application scenario: an Escort Task in a shopping mall for guiding a customer towards a desired point of interest.

We consider stochastic planning problems that involve multiple objectives such as minimizing task completion time and energy consumption. These problems can be modeled as multi-objective Markov decision processes (MOMDPs), an extension of the widely-used MDP model to handle problems involving multiple value functions. We focus on a subclass of MOMDPs in which the objectives have a {\em relaxed lexicographic structure}, allowing an agent to seek improvement in a lower-priority objective when the impact on a higher-priority objective is within some small given tolerance. We examine the relationship between this class of problems and {\em constrained MDPs}, showing that the latter offer an alternative solution method with strong guarantees. We show empirically that a recently introduced algorithm for MOMDPs may not offer the same strong guarantees, but it does perform well in practice.

Flow is a psychological state that is reported to improve people’s performance. Flow can emerge when the person’s skills and the challenges of their activity match. This paper applies this concept to artificial intelligence agents. We equip a decision-making agent with a metacontrol policy that guides the agent to activities where the agent’s skills match the activity difficulty. Consequently, we expect the agent’s performance to improve. We implement and evaluate this approach in the role-playing game of Angband.

This research studies the impact of Time-Of-Use (TOU) tariffs in a competitive electricity market place. Specifically, it focuses on the question of how should an autonomous broker agent optimize TOU tariffs in a competitive retail market, and what is the impact of such tariffs on the economy. We formalize the problem of TOU tariff optimization and propose an algorithm for approximating its solution. We extensively experiment with our algorithm in a large-scale, detailed electricity retail markets simulation of the Power Trading Agent Competition (Power TAC) and: 1) find that our algorithm results in 15\% peak-demand reduction, 2) find that its peak-flattening results in greater profits and/or profit-share for the broker and allows it to win in head-to-head competition against the 1st and 2nd place brokers from the Power TAC 2014 finals, and 3) analyze several economic implications of using TOU tariffs in competitive retail markets.

The problem of comparing the performance of different Real-Time Strategy (RTS) Intelligent Agents (IA) is non-trivial. And often different research groups employ different testing methodologies designed to test specific aspects of the agents. However, the lack of a standard process to evaluate and compare different methods in the same context makes progress assessment difficult. In order to address this problem, this paper presents a set of benchmark scenarios and metrics aimed at evaluating the performance of different techniques or agents for the RTS game StarCraft. We used these scenarios to compare the performance of a collection of bots participating in recent StarCraft AI (Artificial Intelligence) competitions to illustrate the usefulness of our proposed benchmarks.