Showing posts with label Distributed and Swarm Intelligence. Show all posts
Showing posts with label Distributed and Swarm Intelligence. Show all posts

Artificial Intelligence - What Are Non-Player Characters And Emergent Gameplay?


Emergent gameplay occurs when a player in a video game encounters complicated scenarios as a result of their interactions with other players in the game.

Players may fully immerse themselves in an intricate and realistic game environment and feel the consequences of their choices in today's video games.

Players may personalize and build their character and tale.

Players take on the role of a cyborg in a dystopian metropolis in the Deus Ex series (2000), for example, one of the first emergent game play systems.

They may change the physical appearance of their character as well as their skill sets, missions, and affiliations.

Players may choose between militarized adaptations that allow for more aggressive play and stealthier options.

The plot and experience are altered by the choices made on how to customize and play, resulting in unique challenges and results for each player.

When players interact with other characters or items, emergent gameplay guarantees that the game environment reacts.

Because of many options, the tale unfolds in surprising ways as the gaming world changes.

Specific outcomes are not predetermined by the designer, and emergent gameplay can even take advantage of game flaws to generate actions in the game world, which some consider to be a form of emergence.

Artificial intelligence has become more popular among game creators in order to have the game environment respond to player actions in a timely manner.

Artificial intelligence aids the behavior of video characters and their interactions via the use of algorithms, basic rule-based forms that help in generating the game environment in sophisticated ways.

"Game AI" refers to the usage of artificial intelligence in games.

The most common use of AI algorithms is to construct the form of a non-player character (NPC), which are characters in the game world with whom the player interacts but does not control.

In its most basic form, AI will use pre-scripted actions for the characters, who will then concentrate on reacting to certain events.

Pre-scripted character behaviors performed by AI are fairly rudimentary, and NPCs are meant to respond to certain "case" events.

The NPC will evaluate its current situation before responding in a range determined by the AI algorithm.

Pac-Man is a good early and basic illustration of this (1980).

Pac-Man is controlled by the player through a labyrinth while being pursued by a variety of ghosts, who are the game's non-player characters.

Players could only interact with ghosts (NPCs) by moving about; ghosts had limited replies and their own AI-programmed pre-scripted movement.

The AI planned reaction would occur if the ghost ran into a wall.

It would then roll an AI-created die that would determine whether or not the NPC would turn toward or away from the direction of the player.

If the NPC decided to go after the player, the AI pre-scripted pro gram would then detect the player’s location and turn the ghost toward them.

If the NPC decided not to go after the player, it would turn in an opposite or a random direction.

This NPC interaction is very simple and limited; however, this was an early step in AI providing emergent gameplay.

Contemporary games provide a variety of options available and a much larger set of possible interactions for the player.

Players in contemporary role-playing games (RPGs) are given an incredibly high number of potential options, as exemplified by Fallout 3 (2008) and its sequels.

Fallout is a role-playing game, where the player takes on the role of a survivor in a post-apocalyptic America.

The story narrative gives the player a goal with no direction; as a result, the player is given the freedom to play as they see fit.

The player can punch every NPC, or they can talk to them instead.

In addition to this variety of actions by the player, there are also a variety of NPCs controlled through AI.

Some of the NPCs are key NPCs, which means they have their own unique scripted dialogue and responses.

This provides them with a personality and provides a complexity through the use of AI that makes the game environment feel more real.

When talking to key NPCs, the player is given options for what to say, and the Key NPCs will have their own unique responses.

This differs from the background character NPCs, as the key NPCs are supposed to respond in such a way that it would emulate interaction with a real personality.

These are still pre-scripted responses to the player, but the NPC responses are emergent based on the possible combination of the interaction.

As the player makes decisions, the NPC will examine this decision and decide how to respond in accordance to its script.

The NPCs that the players help or hurt and the resulting interactions shape the game world.

Game AI can emulate personalities and present emergent gameplay in a narrative setting; however, AI is also involved in challenging the player in difficulty settings.

A variety of pre-scripted AI can still be used to create difficulty.

Pre scripted AI are often made to make suboptimal decisions for enemy NPCs in games where players fight.

This helps make the game easier and also makes the NPCs seem more human.

Suboptimal pre-scripted decisions make the enemy NPCs easier to handle.

Optimal decisions however make the opponents far more difficult to handle.

This can be seen in contemporary games like Tom Clancy’s The Division (2016), where players fight multiple NPCs.

The enemy NPCs range from angry rioters to fully trained paramilitary units.

The rioter NPCs offer an easier challenge as they are not trained in combat and make suboptimal decisions while fighting the player.

The military trained NPCs are designed to have more optimal decision-making AI capabilities in order to increase the difficulty for the player fighting them.

Emergent gameplay has evolved to its full potential through use of adaptive AI.

Similar to prescript AI, the character examines a variety of variables and plans about an action.

However, unlike the prescript AI that follows direct decisions, the adaptive AI character will make their own decisions.

This can be done through computer-controlled learning.

AI-created NPCs follow rules of interactions with the players.

As players go through the game, the player interactions are analyzed, and some AI judgments become more weighted than others.

This is done in order to provide distinct player experiences.

Various player behaviors are actively examined, and modifications are made by the AI when designing future challenges.

The purpose of the adaptive AI is to challenge the players to a degree that the game is fun while not being too easy or too challenging.

Difficulty may still be changed if players seek a different challenge.

This may be observed in the Left 4 Dead game (2008) series’ AI Director.

Players navigate through a level, killing zombies and gathering resources in order to live.

The AI Director chooses which zombies to spawn, where they will spawn, and what supplies will be spawned.

The choice to spawn them is not made at random; rather, it is based on how well the players performed throughout the level.

The AI Director makes its own decisions about how to respond; as a result, the AI Director adapts to the level's player success.

The AI Director gives less resources and spawns more adversaries as the difficulty level rises.

Changes in emergent gameplay are influenced by advancements in simulation and game world design.

As virtual reality technology develops, new technologies will continue to help in this progress.

Virtual reality games provide an even more immersive gaming experience.

Players may use their own hands and eyes to interact with the environment.

Computers are growing more powerful, allowing for more realistic pictures and animations to be rendered.

Adaptive AI demonstrates the capability of really autonomous decision-making, resulting in a truly participatory gaming experience.

Game makers are continuing to build more immersive environments as AI improves to provide more lifelike behavior.

These cutting-edge technology and new AI will elevate emergent gameplay to new heights.

The importance of artificial intelligence in videogames has emerged as a crucial part of the industry for developing realistic and engrossing gaming.

Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

See also: 

Brooks, Rodney; Distributed and Swarm Intelligence; General and Narrow AI.

Further Reading:

Brooks, Rodney. 1986. “A Robust Layered Control System for a Mobile Robot.” IEEE Journal of Robotics and Automation 2, no. 1 (March): 14–23.

Brooks, Rodney. 1990. “Elephants Don’t Play Chess.” Robotics and Autonomous Systems6, no. 1–2 (June): 3–15.

Brooks, Rodney. 1991. “Intelligence Without Representation.” Artificial Intelligence Journal 47: 139–60.

Dennett, Daniel C. 1997. “Cog as a Thought Experiment.” Robotics and Autonomous Systems 20: 251–56.

Gallagher, Shaun. 2005. How the Body Shapes the Mind. Oxford: Oxford University Press.

Pfeifer, Rolf, and Josh Bongard. 2007. How the Body Shapes the Way We Think: A New View of Intelligence. Cambridge, MA: MIT Press.

Artificial Intelligence - What Is AI Embodiment Or Embodied Artificial Intelligence?


Embodied Artificial Intelligence is a method for developing AI that is both theoretical and practical.

It is difficult to fully trace its his tory due to its beginnings in different fields.

Rodney Brooks' Intelligence Without Representation, written in 1987 and published in 1991, is one claimed for the genesis of this concept.

Embodied AI is still a very new area, with some of the first references to it dating back to the early 2000s.

Rather than focusing on either modeling the brain (connectionism/neural net works) or linguistic-level conceptual encoding (GOFAI, or the Physical Symbol System Hypothesis), the embodied approach to AI considers the mind (or intelligent behavior) to emerge from interaction between the body and the world.

There are hundreds of different and sometimes contradictory approaches to interpret the role of the body in cognition, the majority of which utilize the term "embodied." 

The idea that the physical body's shape is related to the structure and content of the mind is shared by all of these viewpoints.

Despite the success of neural network or GOFAI (Good Old-Fashioned Artificial Intelligence or classic symbolic artificial intelligence) techniques in building row expert systems, the embodied approach contends that general artificial intelligence cannot be accomplished in code alone.

For example, in a tiny robot with four motors, each driving a separate wheel, and programming that directs the robot to avoid obstacles, the same code might create dramatically different observable behaviors if the wheels were relocated to various areas of the body or replaced with articulated legs.

This is a basic explanation of why the shape of a body must be taken into account when designing robotic systems, and why embodied AI (rather than merely robotics) considers the dynamic interaction between the body and the surroundings to be the source of sometimes surprising emergent behaviors.

The instance of passive dynamic walkers is an excellent illustration of this method.

The passive dynamic walker is a bipedal walking model that depends on the dynamic interaction of the leg design and the environment's structure.

The gait is not generated by an active control system.

The walker is propelled forward by gravity, inertia, and the forms of the feet, legs, and inclination.

This strategy is based on the biological concept of stigmergy.

  • Stigmergy is based on the idea that signs or marks left by actions in the environment inspire future actions.


Embodied AI is influenced by a variety of domains. Engineering and philosophy are two frequent methods.

Rodney Brooks proposed the "subsumption architecture" in 1986, which is a method of generating complex behaviors by arranging lower-level layers of the system to interact with the environment in prioritized ways, tightly coupling perception and action, and attempting to eliminate the higher-level processing of other models.

For example, the Smithsonian's robot Genghis was created to traverse rugged terrain, a talent that made the design and engineering of other robots very challenging at the time.

The success of this approach was primarily due to the design choice to divide the processing of various motors and sensors throughout the network rather than trying higher-level system integration to create a full representational model of the robot and its surroundings.

To put it another way, there was no central processing region where all of the robot's parts sought to integrate data for the system.

Cog, a humanoid torso built by the MIT Humanoid Robotics Group in the 1990s, was an early effort at embodied AI.

Cog was created to learn about the world by interacting with it physically.

Cog, for example, may be shown learning how to apply force and weight to a drum while holding drumsticks for the first time, or learning how to gauge the weight of a ball once it was put in Cog's hand.

These early notions of letting the body conduct the learning are still at the heart of the embodied AI initiative.

The Swiss Robots, created and constructed in the AI Lab at Zurich University, are perhaps one of the most prominent instances of embodied emergent intelligence.

Simple small robots with two motors (one on each side) and two infrared sensors, the Swiss Robots (one on each side).

The only high-level instructions in their programming were that if a sensor detected an item on one side, it should move in the other direction.

However, when combined with a certain body form and sensor location, this resulted in what seemed to be high-level cleaning or clustering behavior in certain situations.

A similar strategy is used in many other robotics projects.

Shakey the Robot, developed by SRI International in the 1960s, is frequently credited as being the first mobile robot with thinking ability.

Shakey was clumsy and sluggish, and he's often portrayed as the polar antithesis of what embodied AI is attempting to achieve by moving away from higher-level thinking and processing.

However, even in 1968, SRI's approach to embodiment was a clear forerunner of Brooks', since they were the first to assert that the finest reservoir of knowledge about the actual world is the real world itself.

The greatest model of the world is the world itself, according to this notion, which has become a rallying cry against higher-level representation in embodied AI.

Earlier robots, in contrast to the embodied AI software, were mostly preprogrammed and did not actively interface with their environs in the manner that this method does.

Honda's ASIMO robot, for example, isn't an excellent illustration of embodied AI; instead, it's representative of other and older approaches to robotics.

Work in embodied AI is exploding right now, with Boston Dynamics' robots serving as excellent examples (particularly the non-humanoid forms).

Embodied AI is influenced by a number of philosophical ideas.

Rodney Brooks, a roboticist, particularly rejects philosophical influence on his technical concerns in a 1991 discussion of his subsumption architecture, while admitting that his arguments mirror Heidegger's.

In several essential design aspects, his ideas match those of phenom enologist Merleau-Ponty, demonstrating how earlier philosophical issues at least reflect, and likely shape, much of the design work in contemplating embodied AI.

Because of its methodology in experimenting toward an understanding of how awareness and intelligent behavior originate, which are highly philosophical activities, this study in embodied robotics is deeply philosophical.

Other clearly philosophical themes may be found in a few embodied AI projects as well.

Rolf Pfeifer and Josh Bongard, for example, often draw to philosophical (and psychological) literature in their work, examining how these ideas intersect with their own methods to developing intelligent machines.

They discuss how these ideas may (and frequently do not) guide the development of embodied AI.

This covers a broad spectrum of philosophical inspirations, such as George Lakoff and Mark Johnson's conceptual metaphor work, Shaun Gallagher's (2005) body image and phenomenology work, and even John Dewey's early American pragmatism.

It's difficult to say how often philosophical concerns drive engineering concerns, but it's clear that the philosophy of embodiment is probably the most robust of the various disciplines within cognitive science to have done embodiment work, owing to the fact that theorizing took place long before the tools and technologies were available to actually realize the machines being imagined.

This suggests that for roboticists interested in the strong AI project, that is, broad intellectual capacities and functions that mimic the human brain, there are likely still unexplored resources here.

Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

See also: 

Brooks, Rodney; Distributed and Swarm Intelligence; General and Narrow AI.

Further Reading:

Brooks, Rodney. 1986. “A Robust Layered Control System for a Mobile Robot.” IEEE Journal of Robotics and Automation 2, no. 1 (March): 14–23.

Brooks, Rodney. 1990. “Elephants Don’t Play Chess.” Robotics and Autonomous Systems 6, no. 1–2 (June): 3–15.

Brooks, Rodney. 1991. “Intelligence Without Representation.” Artificial Intelligence Journal 47: 139–60.

Dennett, Daniel C. 1997. “Cog as a Thought Experiment.” Robotics and Autonomous Systems 20: 251–56.

Gallagher, Shaun. 2005. How the Body Shapes the Mind. Oxford: Oxford University Press.

Pfeifer, Rolf, and Josh Bongard. 2007. How the Body Shapes the Way We Think: A New View of Intelligence. Cambridge, MA: MIT Press.

What Is Artificial General Intelligence?

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