Rethinking NPC intelligence

Non-Player Characters (NPCs) are critical to the interactive and dynamic character of modern video games. They inhabit game worlds, engage with players, and play a large role in storytelling and gameplay mechanics. Yet, conventional rule-based NPCs tend to fall short of realism, flexibility, and scalability needed to keep pace with changing player expectations and sophisticated game worlds. This paper discusses the intersection of Artificial Intelligence (AI) engineering techniques with game development to create intelligent, adaptive NPCs. Through modular AI architectures, reinforcement learning, behaviour cloning, and effective deployment strategies, this research presents a complete framework for developing intelligent NPCs that provide increased interactivity and engagement. It also discusses the application of memory repositories to preserve NPC contextual awareness across extended interactions. Comparative analysis and case studies are employed to illustrate enhancements in gameplay dynamics and player experience.

Recently, there are many researches about NPC (Non-player character) which are appeared in many games. There is a little study which has view of the speech of NPC, but most of them are focused on the movement of NPC. Most of NPC say the same line repeatedly, that make the game unrealistic. In the paper, we construct the quest ontology of game World of Warcraft and collect the articles of Q/A bulletin board which is from the web site, also called community, of the game. With the game ontology and the article from the bulletin, we construct a corpus consisted of game keywords as NPC knowledge. In the runtime, when a player asks a question to NPC, it can find the answer by comparing the question and its own knowledge.

Current computer games are being set in increasingly more complex and dynamic virtual environments. Massively multiplayer online games, for example, are played in persistent virtual worlds, which evolve and change as players create and personalize their own virtual property. In contrast, technologies for controlling the behavior of nonplayer characters that populate virtual game worlds are frequently limited to preprogrammed rules. Characters using fixed rule-sets lack the ability to adapt in time with their environment. Motivated reinforcement learning offers an alternative to character design that can achieve nonplayer characters that both evolve and adapt in dynamic environments. This article presents and evaluates two computational models of motivation for use in nonplayer characters in persistent computer game worlds. These models represent motivation as an ongoing search for novelty, interest, and competence. Two metrics are introduced to evaluate the adaptability of characters controlled by motivated reinforcement learning agents using different models of motivation. These metrics characterize the behavior of nonplayer characters in terms of the variety and complexity of learned behaviors. An empirical evaluation of characters in simulated game scenarios shows that characters motivated by the search for competence are more adaptable in dynamic environments than those motivated by interest and novelty alone.

In modern computer games, a NPC (non-player character) can make choices. In the previous researches, a NPC can have the certain knowledge in a game world; this will reduce the believability of perception for a NPC. A decision model based on Bayesian network and fuzzy logic for a NPC is set up in this paper; a NPC can partially know domain knowledge. A behavior animation of a virtual dog by the model is realized on PC, the results show that the virtual dog can behave believable in watching a house.

Many computer games use custom scripts to control the ambient behaviors of non-player characters (NPCs). Therefore, a story writer must write fragments of computer code for the hundreds or thousands of NPCs in the game world. The challenge is to create entertaining and non-repetitive behaviors for the NPCs without investing substantial programming effort to write custom non-trivial scripts for each NPC. Current computer games have simplistic ambient behaviors for NPCs; it is rare for NPCs to interact with each other. In this paper, we describe how generative behavior patterns can be used to quickly and reliably generate ambient behavior scripts that are believable, entertaining and non-repetitive, even for the more difficult case of interacting NPCs. We demonstrate this approach using BioWare's Neverwinter Nights game.

Non-player characters (NPCs) in games are traditionally hard-coded or dependent on pre-specified goals, and consequently struggle to behave sensibly in ever-changing and possibly unpredictable game worlds. To make them fit for new developments in procedural content generation, we introduce the principle of Coupled Empowerment Maximisation as an intrinsic motivation for game NPCs. We focus on the development of a general game companion, designed to support the player in achieving their goals. We evaluate our approach against three intuitive and abstract companion duties. We develop dedicated scenarios for each duty in a dungeon-crawler game testbed, and provide qualitative evidence that the emergent NPC behaviour fulfils these duties. We argue that this generic approach can speed up NPC AI development, improve automatic game evolution and introduce NPCs to full game-generation systems.

Role-playing games (RPGs) rely on interesting and varied experiences to maintain player attention. These experiences are often provided through quests, which give players tasks that are used to advance stories or events unfolding in the game. Traditional quests in video games require very specific conditions to be met, and for participating members to advance them by carrying out pre-defined actions. These types of quests are generated with perfect knowledge of the game world and are able to force desired behaviors out of the relevant non-player characters (NPCs). This becomes a major issue in massive multiplayer online (MMO) when other players can often disrupt the conditions needed for quests to unfold in a believable and immersive way, leading to the absence of a genuine multiplayer RPG experience. Our proposed solution is to dynamically create quests from real-time information on the unscripted actions of other NPCs and players in a game. This thesis shows that it is possible to create logical quests without global information knowledge, pre-defined story-trees, or prescribed player and NPC behavior. This allows players to become involved in storylines without having to perform any specific actions. Results are shown through a game scenario created from the Panoptyk Engine, a game engine in early development designed to test AI reasoning with information and the removal of the distinction between NPC and human players. We focus on quests issued by the NPC faction leaders of several in-game groups known as factions. Our generated quests are created logically from the pre-defined personality of each NPC leader, their memory of previous events, and information given to them by in-game sources. Long-spanning conflicts are seen to emerge from factions issuing quests against each other; these conflicts can be represented in a coherent narrative. A user study shows that players felt quests were logical, that players were able to recognize quests were based on events happening in the game, and that players experienced follow-up consequences from their actions in quests.

From Sensing Shape to Shaping Sense: A Dynamic Model of Absorptive Capacity and Selective Revealing

Market relations are not confined to free competition and price-making mechanisms. The market is an instituted process (Polanyi 1992). It is constituted by sets of rules, regulations, and other institutional arrangements, including relations of trust. Starting with this general notion, we define trust as a belief that other agents will act in a predictable way and fulfill their obligations without special sanctions (Coleman 1988). Specifying this notion, we differentiate between two levels through which trust relationships can develop. The first level is achieved through the predictability of the behavior of other actors. The second level is reached through mutual obligations to follow accepted conventions, which are voluntarily taken by market actors.We also employ the distinction between one-sided trust in institutions and reciprocal trust among business actors (Rose-Ackerman 2001 a,b).

Most textbooks on organisations deal with the political aspects of organisational life in terms of power relationships and conflict and conflict resolution. This chapter takes a rather different starting place in that it deals with the micropolitics of interaction. Of course, in practice, these are embedded in the wider network of social relationships, as will be apparent from the case study. By giving attention to motive, the case study aims to give students the opportunity to explore a multiplicity of different standpoints that have a bearing on a situation and its interpretation. The case invites students to explore the motivations behind a range of different positions described in the case. It also asks students to reflect on their own experiences and motivations. The primary objective of the case study is to make students aware of the importance of being able to ascribe motives in order to understand the behaviour and, in this specific instance, the political behaviour of other actors in an organisational and domestic setting. Being able to read the motives of others and to predict their reactions to situations on the basis of that reading is a considerable political skill and one which affords some protection and comfort in the complexities of organisational life.

In regard to international relations (IR), power is understood as the ability of state or other political actors to impose its own control or influence over other state(s) or other political actors, or at least to influence the outcome of events on the local, regional or global level. Power politics as a phenomena has two dimensions: internal and external. The internal dimension is applied in the inner policy of the state and the external in the foreign affairs or outside of the home politics. The powerfulness of a state depends on its real independence or sovereignty from outside influence on both formulation and realization of its own policy. The internal power is represented by the level of autonomy in the inner affairs while the external power corresponds to capacity to control the behaviour and influence from the outside in domestic affairs and to influence by itself the affairs and politics of the others. However, a majority of researchers suggest that power politics mostly means the potential capacity and practical ability to influence the behaviour of other actors in IR in accordance with its own aims calculated into the framework of public or secret national interests.

Ian Manners (2002) famously argued that the European Union (EU) is a ‘normative’ power. According to this description, ethical values are fundamental both to the legal basis, and to the day- to-day policies of the EU. This essay evaluates the claim that the EU is a Normative Power, focusing on the field of human rights. Certainly, the EU strongly promotes its human rights policies as being a force for good in the world. The EU’s has traditionally been supportive of international legal regimes, and its human rights values have conditioned its relations with other actors. Despite this, the EU’s policies have often failed to change the behaviour of other actors. The main cause of this gap between rhetoric and reality is the conflict between the traditional realist interests of member states, and the ideals of the EU. The EU must be more conscious of this clash, if its human rights policies are to be successful.

The Nature of Supreme Court Power. By Matthew E. K. Hall. New York: Cambridge Univ. Press, 2011. 262 pp. $94.00 cloth.Merely Judgment: Ignoring, Evading, and Trumping Supreme Court. By Martin J. Sweet. Charlottesville and London: Univ. of Virginia Press, 2010. 240 pp. $35.00 clothMatthew E. K. Hall's The Nature of Supreme Court Power and Martin J. Sweet's Merely Judgment are impressive books considerably advance our knowledge about implementation and impact of Supreme Court decisions. Hall frames his study as a direct response to Gerald Rosenberg's thesis Court (and generally) 'can almost never be effective producers of significant social reform' (xiii; 160, quoting Rosenberg 2008: 422). Moreover, Hall argues, Rosenberg's view is emblematic of decided weight of scholarly authority on nature of Court's power. The Court is generally seen as a highly constrained and weak institution (13-15). By contrast, Hall finds that Court possesses remarkable power to alter behavior of [other] actors in a wide range of policy (160). Sweet wants to make a different argument. He claims political institutions enjoy considerable discretion in deciding whether and how to follow Court, because they can often defang would be plaintiffs (5). Obviously, both authors cannot be right. As is often case, as between Hall and Sweet, different theoretical approaches play a part in competing interpretations. Notwithstanding different approaches in play here, it seems to me for most part, Hall is right, and Sweet is wrong, although Sweet's study is well done and informative in many ways.Like Rosenberg, Hall is committed to social scientific positivism. Through an objective case selection method (27, and Appendix I), he pinpoints no less than 57 important cases decided between 1954 and 2006, which he then groups into 27 issue areas for purposes of impact analysis. Hall's conception of power, like Rosenberg's, is one-dimensional: 'A power relation, actual or potential, is an actual or potential relation between preferences of an actor regarding an outcome and outcome itself ' (7, quoting Nagel 1975). Hall's study, notwithstanding its incredible scope, is limited to seeking to observe whether Court changes behavior of other actors in cases in which it actually attempts to do so by striking down laws.The question is Under what conditions is Court powerful? (14). The independent variables are twofold: (1) the context of a decision, and (2) degree of popular opposition to a decision. The first of these brings us to Hall's great insight. Vast research demonstrates lower are generally faithful and reliable implementers of Court decisions (16). By institutional Hall means the distinction between Supreme Court rulings can and cannot be implemented by lower courts (5). He calls issues for which lower are primary in implementation process issues, and issues for which they are not issues. He codes every case as either vertical or lateral. For popular opinion, Hall relies exclusively on public opinion data. This elegant model thus yields a two-by-two framework for inquiry. Hall hypothesizes only very strong popular opposition in a lateral issue can defeat Court when it intends to alter behavior, and it follows Court can prevail over public opposition (even very strong opposition) in vertical cases, but not lateral ones. It then remains for Hall to specify relevant intended outcomes of Court's decisions, and to find good data can show us whether actors changed their behavior in response to them. Hall is well aware his analysis is shot through with interpretation and judgment calls. To his credit, he does not try to standardize his measures across cases, but rather clearly lays out his thinking about what research and information is available, which indicators or measures make most sense in a given context, and so on. …

Creating detailed and interactive game environments is an area of great importance in the video game industry. This includes creating realistic Non-Player Characters which respond seamlessly to the players actions. Machine learning had great contributions to the area, overcoming scalability and robustness shortcomings of hand-scripted models. We introduce the early results of a reinforcement learning approach in building a simulation environment for heterogeneous, multi-agent non-player characters in a dynamic road network game scene.

The development of the gaming industry has provided an opportunity to combine entertainment with education. In this context, this study aims to implement the Jump Point Search (JPS) algorithm in the game "Prince of Walangsungsang" to provide a more interesting playing experience and introduce players to the history of Prince Walangsungsang journey. This study focuses on the action role-playing game (RPG) genre that combines game elements with historical stories. In making games, the development method used is the Game Development Life Cycle (GDLC) to ensure structured and efficient development steps. The JPS algorithm is implemented as an artificial intelligence (AI) agent on non-player characters (NPC) in games. By implementing the JPS algorithm, in-game NPCs can optimize their movements in a complex game environment, speed up finding the shortest path, and avoid unnecessary routes, as well as avoid dynamic obstacles. Games "Prince of Walangsungsang" provide a more realistic and challenging gaming experience for players, as well as introduce historical aspects through interactions with NPCs. The results of the study show that the implementation of the JPS algorithm in the game "Prince of Walangsungsang" improves the efficiency and intelligence of NPCs, so that players can experience a more dynamic and interesting playing experience. In addition, game-based introduction to the history of Prince Walangsungsang also gives players the opportunity to learn about and appreciate the cultural heritage associated with this historical figure.