Life is like a grammar! Once you recognize and learn the patterns you've figured the grammar out. We create these patterns, thus we create the grammar. They manifest themselves differently in different people and in different aspects of life, but they're always there.
It's so much fun when in learning about one thing, you learn something completely disparate in the process.
FOLLOW THE GRAMMAR!!
Deciding what my thesis topic should be - Fun.
Figuring out what areas will help me in my research - Fun.
Researching for my chosen topic - Fun.
Reading the research I found - Fun.
Outlining what I'm going to write about - Fun.
Actually writing my thesis - Not Fun!
Brought to you by the Music Genome Project: Pandora. Customize your own internet radio station based on genres, artists and songs you like. Popular or obscure, you'll probably be able to find it here. And based on the artist or song you give it, it will find other artists and songs that are similar. This is by far the coolest thing I've found in a long time. It's totally customizable, tell it what you like, tell it what you don't like, save stations you've created and share them with others. I found it awhile ago, and have been meaning to share it. A lot of you have probably already discovered it and tried it out, but if you haven't you should definitely take a look.
So they're not brand spanking new, but they're still new none the less.
Laptop:
HP dv6000 series
Intel Duo T2050 @ 1.60GHz
1 GB of RAM
nVIDIA geforce go 7400
100 GB hard drive
It's really shiny, though the pictures don't do it justice. The finish is shiny black with this neat wave pattern on it. So pretty.
My other new gadget is my (PRODUCT)RED 8GB iPod nano. I love it. It's so cute, and so small, and best of all it's red!
I just wish the pictures were better. The cellphone camera doesn't quite capture the red of the iPod, or the cool pattern of the laptop case.
So, lately I've found some rather interesting and fun stuff on the internet. It's mostly pretty random, but some of it's kind of cool.
Oooh, a fairy.
Aw, look at the cute little puppy in the bag!
I've forgotten to post the last few weeks, so here's the update.
Sheldon, Lee. Chapter 3. “Character Development and Storytelling.” Boston: Thomson Course Technology PTR, (2004), 37-59.
This chapter discusses the creation of a three-dimensional character, and respecting that character. The three dimensions are: physical, how the character looks; sociological, the character’s past, environment, culture; psychological, the character’s attitudes, opinions, and view of the world. Another key is making characters aware, but not to the extent that they are telling the player about themselves. The character’s actions should tell the player about itself. Sheldon also points out the need for the character to grow and develop, and that they’re not the same thing. Growth is changes that happen to the character over the course of the narrative. Development is the revealing of the character’s characteristics over the course of the narrative. Another thing Sheldon focuses on is not stereotyping a character into a certain role or persona.
Sheldon, Lee. Chapter 4. “Character Development and Storytelling.” Boston: Thomson Course Technology PTR, (2004), 61-85.
Sheldon discuss the different roles characters, specifically NPCs, fill. NPCs are what drive the narrative in games; they also give the game-world life. NPCs can also provide commentary and gossip about other characters as well as goings no in the world. Sheldon emphasizes giving NPCs multiple roles, or functions, with in the game causing them to be deeper characters. He also discusses the different roles NPCs can fulfill such as mentors, trainers, sidekicks, merchants, and quest givers. There is also the villain, or antagonist, role. The villain is the player-character’s opponent, and needs to be designed to be an equal, so to present come challenge to the player.
Sheldon, Lee. Chapter 5. “Character Development and Storytelling.” Boston: Thomson Course Technology PTR, (2004), 87-112.
In this chapter, Sheldon talks about NPC mobility, emotion and memory. Mobility is important to create a more realistic world. Static NPCs, ones who never move, are uninteresting and won’t be as capable of filling multiple roles in the game and narrative. Mobile NPCs doesn’t mean that the NPCs need to be able to move throughout the entire game-world, though some may. Some may be confined to a specific area, but are able to move around within that area. Sheldon also discusses mobility in terms of the NPC’s response to the player-character, some may shy away, or tremble in fear, while others will challenge the PC’s space. This type of mobility helps convey to the player the NPC’s personality.
Sheldon, Lee. Chapter 6. “Character Development and Storytelling.” Boston: Thomson Course Technology PTR, (2004), 113-148.
Sheldon talks a lot about dialogue, and how dialogue functions in games. He focuses more how the exchange rather than the creation of dialogue, but also touches on how the exchange can be perceived. Something else Sheldon touches on is character relationships. He presents a relationship system, the TGT Full System, which creates a base personality and facilitates creating dynamic relationships between characters. The base personality is comprised of six areas: like, respect loyalty, trust, admiration and love. A relationship modifier is applied to the base personality to calculate the relationship between two characters.
Littlejohn, Randy. “Agitating for Dramatic Change.” Gamasutra 29 Oct. 2003. 3 Nov. 2006.
This article discusses a potential new age of video games. Littlejohn wants to create interactive drama, like video games but with more focus on story, character development, etc., for the masses. These games would be immersive, familiar like TV and film, have empathetic characters, and have intuitive interfaces. He talks a lot about the characters in these games, synthespians. Synthespians are “autonomous agents with goals, biases, and abilities who carry out apparent 'intent'.” That's not to say they have any actual understanding, or posses any actual cognitive ability, but to the player they are intelligent, goal oriented, communicative, emotional NPCs. He also discusses some toolsets that could be created that would enable game designers and others to create such characters with out being proficient at programming or AI. He also goes on to discuss Facade, a game from Michael Mateas and Andrew Stern. Facade is an experiment in electronic narrative, or storytelling, and personally I think it's a step in the right direction for games.
Funge, John. “AI for Games and Animation: A Cognitive Modeling Approach.” Gamasutra 6 Dec. 1999. 3 Nov. 2006.
Funge discusses another approach to modeling synthetic characters. It's fairly strait forward, and similar to other models being done by other researchers. The most interesting part is he's using situation calculas which is a mathematical logic notation. It's an “AI formalism for describing changing worlds using sorted first-order logic.” Fluents are any world property that can change over time, and can be described as a function with a situation as it's last argument. Precondition axioms define what state the world must be in for an action to be preformed. Effect axioms define the conditions of a fluent to take after an action has occurred. Funge uses this in his model to create predefined behaviors and goal-directed behaviors. He also discusses different methods for the character to determine it's actions such as HFSMs, and situation trees.
Reynolds, Craig W. “Steering Behaviors For Autonomous Characters.” Proc. of Game Developers Conference, 1999, San Francisco.
Reynolds discusses many different steering behaviors for autonomous characters. These include: seek, flee, pursuit, evasion, offset pursuit, arrival, obstacle avoidance, wander, path following, wall following, containment, flow field following, unaligned collision avoidance, separation, cohesion, alignment, flocking, and leader following. He gives a description of what each steering type is and to some extent how it works. For example, flow field following directs the motion of the character based on their position in the environment. A flow field is implemented using flow vectors that direct the character's movement. This is something that can be used in games to direct NPCs through the world. He also talks about combining different steering behaviors, because on their own they would seem rudimentary, to create more complex steering behaviors. One method he proposes is prioritizing the steering behaviors, such that when one fails the next one takes effect.
Vosinakis, Spyros, and Themis Panayiotopoulos. “SimHuman: A Platform for Real-Time Virtual Agents with Planning Capabilities.” Proc. Intelligent Virtual Agents: Third International Workshop, 2001, Madrid.
This article rehashed a lot of material discussed in previous readings. Mildly interesting was the section on dynamic actions. Their dynamic actions are things like path planning and obstacle avoidance, and this is done by doing ray casting. More complex dynamic actions, like moving an object, use inverse kinematics. The one thing they don't discuss how the character knows what animated sequences to create.
Prada, Rui, and Ana Paiva. “Believable Groups of Synthetic Characters.” Proc. of the 4th International Joint Conference on Autonomous Agents and Multiagent Systems, 2005, Netherlands.
This article discusses modeling the behavior of groups of synthetic characters. Prada and Paiva have developed a model, the Synthetic Group Dynamics Model (SGD Model), to give a synthetic character awareness of the group it belongs to and the other members of that group. This allows the character to build relations with other members and build proper social models. They've broken the model down into four different levels. The individual level provides each character with an identity, skills, and a personality consisting of extroversion and agreeableness. The group level defines the structure of the group and agent's attitudes toward the group. The structure comes from members social influence and social attraction to other members. The interactions level categorizes possible interactions and their impact on the group.There are socio-emotional interactions and instrumental interactions. Finally, the context level defines the environment that the characters exist in. Prada and Paiva have developed a game, “Perfect Circle: the Quest for the Rainbow Pearl”, where the player acts as a member of a group of Alchemists looking for the rainbow pearl. They conducted an experiment with three different situations, first where the SGD Model wasn't used, second used the SGD Model with a neutral group, and third used the SGD Model with a negative group. They found that situations that used the SGD Model elicited more trust from the player and more identification.
Kim, Yong-Duk, Jong-Hwan Kim, and Yong-Jae Kim. “Behavior Selection and Learning for Synthetic Character.” Congress on Evolutionary Computation 1 (2004): 898-903.
The authors present a different approach to behavior selection and learning for synthetic characters. Instead of the more common tree structure they have devised behavior selection that chooses behavior probabilistically and deterministically, while learning occurs through adjusting weight between inputs and internal logic. For learning to occur, behaviors are categorized by similarity, when the character does something right it is rewarded, or penalized if it did something wrong, this reward or punishment changes the weights between the input and the behavior.
Blumberg, Bruce. “Action-Selection in Hamsterdam: Lessons from Ethology.” Proc. of the 3rd International Conference on the Simulation of Adaptive Behavior, 1994.
Blumberg aims to devise a action-selection system that allows for the control of the temporal aspects of behavior. This allows for a balance between dithering between activities and pursuing a goal to the detriment of other goals. The system also needs a loose hierarchical structure and a mechanism for sharing information, as well as flexible means to model motivations. Blumberg takes lessons from ethology to design the action-selection system. He also created Hamsterdam, toolkit for developing artificial animals in a 3D environment. In this environment there are Hamsters and Predators. The Hamsters act much like real hamsters, and the predators are “generic”. At Siggraph '93 he used Alive to present Hamsterdam and allow users to interact in the environment.
Burke, Robert, and Bruce Blumberg. “Using an Ethologically-Inspired Model to Learn Apparent Temporal Causality for Planning in Synthetic Creatures.” Proc. of the 1st International Joint Conference on Autonomous Agents and Multiagent Systems, 2002, Bologna.
Built on previous work of the Synthetic Characters group, Burke and Blumberg have create a new cognitive architecture inspired by Scalar Expectancy Theory (SET) and Rate Estimation Theory (RET) proposed by Gallistel and Gibbon. Burke and Blumberg's action selection process is interesting. Creatures have the option of exploiting their knowledge of the world, exploring the world, or reacting to observed stimuli, but these actions need to appear relevant, persistent and coherent. At the end of each timestep the creature should have chosen its action, the object of its attention, and the target object. The three action options are mutually exclusive for the current timestep.
Le Hy, Ronan, et al. “Teaching Bayesian Behaviors to Video Game Characters.” Robotics and Autonomous Systems 47 (2004): 177-185.
Le Hy et al. discuss their work developing synthetic characters using the Unreal Engine and Bayesian modeling. Their Bayesian model consists of a description and a question. Descriptions relevant variables, decomposition and parametric forms for joint distributions. Questions parsed into searched, known and free variables. They did two tests. The first they hand calculated the probability distributions by hand. While this allows for tuning, it doesn't complete the goal of the synthetic character being capable of learning individual players' play styles. Their second test inferred probabilities from actions occurring within the game. They accomplish this by allowing the synthetic character to identify the player's low-level actions, evaluate them to change its behavior. They found that in doing this they could create a synthetic character that would perform just as well, if not a little better than the existing AI in Unreal Tournament.
DeLeon, Victor, and Robert Berry, Jr.. “Bringing VR to the Desktop: Are You Game?” IEEE Multimedia 7.2 (2000): 68-72.
DeLeon and Berry discuss their work creating the Virtual Reality Notre Dame (VRND). Their goal was to make this accessible via the internet allowing people to access it from computers all over the world, as opposed to their Virtual Florida Everglades museum exhibit. Using the Unreal engine they modeled Notre Dame and identified important parts and structures within of interest to visitors. They also created a virtual tour guide to interact with the visitors and give them information about Notre Dame. Unfortunatly the URL they listed for VRND is inactive.
Magerko, Brian, et al. “AI Characters and Directors for Interactive Computer Games.” Proc. of 2004 Innovative Applications of Artificial Intelligence Conference, 2004, San Jose.
Magerko et al. discuss interactive computer games from the view point of interactive story telling. To accomplish this they developed a story director and AI characters as actors. The actors have physical drives and respond to many different physiological effects. They also environment sensing, and are also designed to take direction from the story director so the plot stays on course while they are still allowed to pursue their own goals. The story director keeps track of plot points and pre and post conditions needed for plot points to occur and that the occur at the right time. The story director also helps keep the synthetic characters on task as far as executing the plot while still allowing them to pursue their goals. In their game, Haunt 2, as the player you play a ghost and your end goal is to discover who killed you. But being a ghost you cannot directly interact with the characters or objects, instead you need to find means of getting the characters to intervene for you. One method of doing this is to temporarily possess one of them as long as they're not too scared. While doing so you can influence their thoughts and actions, but you have to be careful so they don't become scared and expel you.
Cavazza, Marc, Fred Charles, and Steven J. Mead. “ Interacting with Virtual Characters in Interactive Storytelling.” Proc. of the First international Joint Conference on Autonomous Agents and Multiagent Systems: Part 1, 2002, Bologna.
This article also discusses interactive storytelling, though it is not as well explained as the previous article, or in my opinion as interactive for the user. They've developed their synthetic characters with Hierarchical Task Networks (HTN) that represents that character's contribution to the plot. The character traverses it's network to progress it's part of the plot, when it comes to a solution node it can not execute it will back up and try another solution node attached to the same goal. The user can interact by moving objects with narrative significance, or can give language instructions/suggestions to the synthetic characters. The user isn't actually part of the plot.
Eccles, Jacquelynne S., and Allen Wigfield. “Motivational Beliefs, Values and Goals.” Annual Review of Psychology. 53 (2002): 109-132.
Eccles and Wigfied review modern theories for motivation, specifically in relation to beliefs, values, and goals. I found their sections focusing on reasons for engagement and integrating motivation and cognition especially interesting. Intrinsic motivations are a reason for engagement, motivation occurs because of interest in the activity. Eccles and Wigfield present a few theories here: self-determination theory , flow theory, and individual difference of intrinsic motivation. They also presented goal theories which they break down into ego-involved goals, maximizing positive results and minimizing negative results, and task-involved goals, mastering tasks to improve the individual. In the integrating motivation and cognition section the authors present theories about self-efficacy and self-regulation.
Grand, Stephen, and Dave Cliff. “Creatures: Entertainment Software Agents with Artificial Life.” Autonomous Agents and Multi-Agent Systems. 1.1 (1998): 39-57.
Grand and Cliff present the work done on the game Creatures where the player interacts in real-time with synthetic creatures in a virtual environment. These creatures have “artificial neural networks for sensory-motor control and learning, artificial biochemistries for energy metabolism and hormonal regulation of behavior, and both the network and the biochemistry are 'genetically' specified to allow for the possibility of evolutionary adaptation through sexual reproduction.” The paper goes on to describe how these functions work. One of the more interesting aspects is how they designed their neural network and brain model. The neural network is divided into nine lobes interconnected by synapses. Each lobe consists of neurons with similar characteristics. The brain model regulates information flow through the lobes. Though it's fairly simple in its construction and is behaviorialist, it provides a logic for how the creature processes information and chooses actions.
Peters, C., and C. O'Sullivan. “ Synthetic Vision and Memory for Autonomous Virtual Humans.” Computer Graphics Forum. 21.4 (2002): 743-753.
Peters and O'Sullivan present a model that combines memory and synthetic vision to give synthetic agents realistic knowledge about the location of objects in the world. Synthetic vision consists of two modes. Distinct vision mode, which false-colors each object with a unique color which is used to look up the object in the scene database. Grouped vision mode objects are false-colored with group colors and is used for lower detail perception and only provides information for visible objects. Since it is unrealistic for memory model to store all objects an agent has come in contact with, filters have been devised to reduce the amount of information stored. The filtering process is done through different types of memory: short-term sensory memory, short-term memory, and long-term memory.
Elliot, Clark. “ Research problems in the use of a shallow Artificial Intelligence model of personality and emotion.” Proc. of the 12th National Conference on Artificial Intelligence, 1994, Seattle. 9-15.
Largely this article spends less time discussing research problems and more time discussing the work being done by the author. Throughout this he does raise interesting ideas, one of them being that users want to express emotions to a computer given that the computer gives a believable illusion of comprehension. We want to anthropomorphise the computer, or the agents in the virtual environment we're interacting with.
Perron, Bernard. “A Cognitive Psychological Approach to Gamplay Emotions.” Proc. of International DiGRA Conference, 2005, Vancouver.
Perron discusses the emotions that arise from gameplay. There are fiction emotions, empathetic emotions, elicited in the player from the observer's position. There are artifact emotions are the emotions elicited from the technology and imagery used to make the game. These types of emotions are existing definitions taken from the film industry. Perron introduces a new type of emotion, gameplay emotions. He describes gameplay emotions as “the emotions arising from our actions in the game” and “the consequent reactions of the game(-world).” The rest of the article becomes rather repetitive and similar to other articles about emotion. I feel that the important thing is that the creation of synthetic characters has the potential to elicit deeper levels of gameplay emotion.
Isbister, Katherine. Chapter 6. “Better Game Characters By Design” New York: Elsevier, 2005.
In this chapter Isbister the role of body language in social interactions and how it applies to video games. Bodies communicate what our relationships are with others, and aspects of our personalities, current emotions and moods. She comes to a conclusion that the use of movement in games isn't as fully utilized as it could be, and gives some pointers about how to better incorporate movement and body language in video game characters. I find this interesting because there are a lot of applications for this in synthetic character creation. Characters with well developed body language will appear to be more realistic and will be more believable.
Laird, John, and Michael van Lent. “Human-level AI's Killer Application: Interactive Computer Games.” Proc. of the Seventeenth National Conference on artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence, 2000.
Laird and van Lent discuss the research and innovation possibilities of AI, specifically human-level AI, in computer and video games. Currently in academia development of AI has been split into specialized areas. Though this has been successful, the authors feel that progress toward human-level AI has been ignored. Because of the growing realism in computer games necessitating for more complex behavior and interactions with the characters, thus they feel that this is the ideal environment for development. There are many roles AI plays in video games such as enemies and opponents, partners, support characters to name a few.
Macedo, Luís, and Amílcar Cardoso. “Exploration of Unknown Environments with Motivational Agents.” Proc. of the Third International Joint Conference on Autonomous Agents and Multiagent Systems – Vol. 1, 2004, New York.
Macedo and Cardoso discuss exploration of environments and the motivations that drive exploration. The motivations they focus on are surprise, curiosity, and hunger. Their agents have a map of areas they've explored, memory about entities (objects in the world) and plans (tasks). Their motivations are applied to the information they receive about the world and they select the appropriate action based on their motivations and memory. This is all well and fine, but it seems that the motivation aspects were under explored.
