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).

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.

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.

Storytelling can be used by conversational agents in a wide variety of domains to maintain user engagement, both within a single interaction and over dozens or hun- dreds of interactions over time. The majority of agents designed with this ability to date deliver their stories as monologues without user input. However, people rarely tell stories in conversations this way, and instead rely on listener contributions to guide the storytelling process. Corpus-based studies of human-human conversational storytelling have demonstrated greater engagement, in the form of longer stories, when listeners co-construct stories this way. We describe a research framework for the generation and evaluation of co-constructed social stories in the context of task-based conversations, and a study on the effects of degree of user-agent story co-construction on user engagement. We find that users are more en- gaged with storytelling agents that allow them to co- construct stories in a contentful manner by asking ques- tions, compared to co-construction through acknowl- edgments only.

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.

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.

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.

We introduce Playspecs, an application of omega-regular expressions to specifying play traces (sequences of game states or events unfolding over time). This connects the automated analysis and model checking of games to the literature on formal software verification via Bu ̈chi automata. We show how to define desirable or undesirable sequences of game events with Playspecs and how associated algorithms can find examples (or prove the impossibility) of such sequences. Playspecs have two main benefits over existing techniques for specifying the behaviors of a game over time. First, they offer a scalable commitment to formal modeling: the same Playspecs can filter existing traces gathered by telemetry, search for satisfying traces using existing game code, or drive formal verification when paired with a logical model of a game. Second, Playspecs' syntax can be customized for the game engine or game in question so designers may write specifications using their game's native vocabulary. We define Playspecs' syntax and semantics (modulo gamespecific customizations) and outline algorithms for each of the applications mentioned above, providing examples from the social simulation game Prom Week and the puzzle game engine PuzzleScript.

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.

Displaying believable emotional reactions in virtual characters is required in applications ranging from virtual-reality trainers to video games. Manual scripting is the most frequently used method and enables an arbitrarily high fidelity of the emotions displayed. However, scripting is labour intense and greatly reduces the scope of emotions displayed and emotionally affected behavior in virtual characters. As a result, only a few virtual characters can display believable emotions and only in pre-scripted encounters. In this paper we implement and evaluate a lightweight algorithm for procedurally controlling both emotionally affected behavior and emotional appearance of a virtual character. The algorithm is based on two psychological models of emotions: conservation of resources and appraisal. The former component controls emotionally affected behavior of a virtual character whereas the latter generates explicit numeric descriptors of the character's emotions which can be used to drive the character's appearance. We implement the algorithm in a simple testbed and compare it to two baseline approaches via a user study. Human participants judged the emotions displayed by the algorithm to be more believable than those of the baselines.