Posts Tagged ‘interactive drama’
13
Oct
Posted by cognitivecomputing in Agents, Language, Learning, Opinion, Talks. Tagged: interactive drama, natural language, semantic memory, social learning. Leave a comment
News consumption is a passive experience—reading print or online newspapers, listening to radio shows and podcasts, watching television broadcasts. News producers create, curate, and organize content which consumers absorb passively. With the advent of interactive conversational technologies ranging from chatbots to voice-based conversational assistants such as Amazon Alexa, there is an opportunity to engage consumers in more interactive experiences around news.
At the Computation+Journalism symposium held at Northwestern University this year, Emily Withrow, editor at Quartz Bot Studio and assistant professor at Northwestern’s Medill School of Journalism and I had a fireside chat to share recent technological developments in this area and explore what kinds of conversational news experiences these technologies might enable.
Panel at the 2017 Computation+Journalism Symposium, Northwestern University, Evanston, IL. #cj2017
1
Jun
Posted by cognitivecomputing in Agents, Language, Learning, Opinion, Talks, Web / Web 2.0. Tagged: believable agents, cognitive media, goal-driven learning, information retrieval, interactive drama, Learning, natural language, personality models, problem solving, semantic memory. 3 comments
As we moved from the age of the keyboard, to the age of touch, and now to the age of voice, natural conversation in everyday language continues to be one of the ultimate challenges for AI. This is a difficult scientific problem involving knowledge acquisition, natural language understanding, natural language generation, context modeling, commonsense reasoning and dialog planning, as well as a complex product design problem involving user experience and conversational engagement.
I will talk about why Conversational AI is hard, how conversational agents like Amazon Alexa understand and respond to voice interactions, how you can leverage these technologies for your own applications, and the challenges that still remain.
Variants of this talk presented (click links for video):
Keynote talks at The AI Conference (2017), O’Reilly AI Conference (2017), The AI Summit (2017), Stanford ASES Summit (2017), MLconf AI Conference (2017), Global AI Conference (2016).
Distinguished lectures at Georgia Tech/GVU (2017), Northwestern University (2017).
Keynote panel at Conversational Interaction Conference (2016).
Lightning TED-style talks at IIT Bay Area Conference (2017), Intersect (2017).
14
Nov
Posted by cognitivecomputing in Agents, Language, Learning, Opinion, Web / Web 2.0. Tagged: believable agents, cognitive media, creativity, information retrieval, interactive drama, Learning, meta-reasoning, natural language, personality models, planning, problem solving, semantic memory. Leave a comment
On September 29, 2016, Amazon announced the Alexa Prize, a $2.5 million university competition to advance conversational AI through voice. We received applications from leading universities across 22 countries. Each application was carefully reviewed by senior Amazon personnel against a rigorous set of criteria covering scientific contribution, technical merit, novelty, and ability to execute. Teams of scientists, engineers, user experience designers, and product managers read, evaluated, discussed, argued, and finally selected the ten teams who would be invited to participate in the competition. Wait, make that twelve; we received so many good applications from graduate and undergraduate students that we decided to sponsor two additional teams.
Today, we’re excited to announce the 12 teams selected to compete with an Amazon sponsorship.
READ MORE:
developer.amazon.com/blogs/post/Tx1UXVV4VJTPYTL/announcing-the-sponsored-teams-for-the-2016-2017-alexa-prize
29
Sep
Posted by cognitivecomputing in Agents, Language, Learning, Opinion, Web / Web 2.0. Tagged: believable agents, cognitive media, creativity, information retrieval, interactive drama, Learning, meta-reasoning, natural language, personality models, planning, problem solving, semantic memory. Leave a comment
Artificial intelligence (AI) is becoming ubiquitous. With advances in technology, algorithms, and sheer compute power, it is now becoming practical to utilize AI techniques in many everyday applications including transportation, healthcare, gaming, productivity, and media. Yet one seemingly intuitive task for humans still eludes computers: natural conversation. Simple and natural for humans, voice communication in everyday language continues to be one of the ultimate challenges for AI.
Today, we are pleased to announce the Alexa Prize, a $2.5 million university competition to advance conversational AI through voice. Teams of university students around the world are invited to participate in the Alexa Prize (see contest rules for details). The challenge is to create a socialbot, an Alexa skill that converses coherently and engagingly with humans on popular topics for 20 minutes. We challenge teams to invent an Alexa socialbot smart enough to engage in a fun, high quality conversation on popular topics for 20 minutes.
Are you up to the challenge?
READ MORE:
developer.amazon.com/public/community/post/Tx221UQAWNUXON3/Are-you-up-to-the-Challenge-Announcing-the-Alexa-Prize-2-5-Million-to-Advance-Co
30
Sep
Posted by cognitivecomputing in Game AI. Tagged: case-based reasoning, games, interactive drama, planning. Leave a comment
Interactive narrative systems attempt to tell stories to players capable of changing the direction and/or outcome of the story. Despite the growing importance of multiplayer social experiences in games, little research has focused on multiplayer interactive narrative experiences. We performed a preliminary study to determine how human directors design and execute multiplayer interactive story experiences in online and real world environments. Based on our observations, we developed the Multiplayer Storytelling Engine that manages a story world at the individual and group levels. Our flexible story representation enables human authors to naturally model multiplayer narrative experiences. An intelligent execution algorithm detects when the author’s story representation fails to account for player behaviors and automatically generates a branch to restore the story to the authors’ original intent, thus balancing authorability against robust multiplayer execution.
Read the paper:
Robust and Authorable Multiplayer Storytelling Experiences
by Mark Riedl, Boyang Li, Hua Ai, Ashwin Ram
in Seventh International Conference on AI and Interactive Digital Entertainment (AIIDE-2011).
www.cc.gatech.edu/faculty/ashwin/papers/er-11-06.pdf
19
Aug
Posted by cognitivecomputing in Agents, Game AI, Learning. Tagged: case-based reasoning, creativity, games, interactive drama, meta-reasoning, problem solving, virtual worlds. Leave a comment
Computer games are an increasingly popular application for Artificial Intelligence (AI) research, and conversely AI is an increasingly popular selling point for commercial digital games. AI for non playing characters (NPC) in computer games tends to come from people with computing skills well beyond the average user. The prime reason behind the lack of involvement of novice users in creating AI behaviors for NPC’s in computer games is that construction of high quality AI behaviors is a hard problem.
There are two reasons for it. First, creating a set of AI behavior requires specialized skills in design and programming. The nature of the process restricts it to certain individuals who have a certain expertise in this area. There is little understanding of how the behavior authoring process can be simplified with easy-to-use authoring environments so that novice users (without programming and design experience) can carry out the behavior authoring task. Second, the constructed AI behaviors have problems and bugs in them which cause a break in player expe- rience when the problematic behaviors repeatedly fail. It is harder for novice users to identify, modify and correct problems with the authored behavior sets as they do not have the necessary debugging and design experience.
The two issues give rise to a couple of interesting questions that need to be investigated: a) How can the AI behavior construction process be simplified so that a novice user (without program- ming and design experience) can easily conduct the authoring activity and b) How can the novice users be supported to help them identify and correct problems with the authored behavior sets? In this thesis, I explore the issues related to the problems highlighted and propose a solution to them within an application domain, named Second Mind(SM). In SM novice users who do not have expertise in computer programming employ an authoring interface to design behaviors for intelligent virtual characters performing a service in a virtual world. These services range from shopkeepers to museum hosts. The constructed behaviors are further repaired using an AI based approach.
To evaluate the construction and repair approach, we conduct experiments with human subjects. Based on developing and evaluating the solution, I claim that a design solution with behavior timeline based interaction design approach for behavior construction supported by an understandable vocabulary and reduced feature representation formalism enables novice users to author AI behaviors in an easy and understandable manner for NPCs performing a service in a virtual world. I further claim that an introspective reasoning approach based on comparison of successful and unsuccessful execution traces can be used as a means to successfully identify breaks in player experience and modify the failures to improve the experience of the player interacting with NPCs performing a service in a virtual world.
The work contributes in the following three ways by providing: 1) a novel introspective reasoning approach for successfully detecting and repairing failures in AI behaviors for NPCs performing a service in a virtual world.; 2) a novice user understandable authoring environment to help them create AI behaviors for NPCs performing a service in a virtual world in an easy and understandable manner; and 3) Design, debugging and testing scaffolding to help novice users modify their authored AI behaviors and achieve higher quality modified AI behaviors compared to their original unmodified behaviors.
Read the dissertation:
Construction and Adaptation of AI Behaviors in Computer Games
by Manish Mehta
PhD dissertation, College of Computing, Georgia Institute of Technology, August 2011.
smartech.gatech.edu/handle/1853/42724
17
May
Posted by cognitivecomputing in Game AI. Tagged: case-based reasoning, games, interactive drama. Leave a comment
We apply case based reasoning techniques to build an intelligent authoring tool that can assist nontechnical users with authoring their own digital movies. In this paper, we focus on generating dialogue lines between two characters in a movie story. We use Darmok2, a case based planner, extended with a hierarchical plan adaptation module to generate movie characters’ dialogue acts with regard to their emotion changes. Then, we use an information state update approach to generate the actual content of each dialogue utterance. Our preliminary study shows that the extended planner can generate coherent dialogue lines which are consistent with user designed movie stories using a small case base authored by novice users. A preliminary user study shows that users like the overall quality of our system generated movie dialogue lines.
Read the paper:
A Case Base Planning Approach for Dialogue Generation in Digital Movie Design
by Sanjeet Hajarnis, Christina Leber, Hua Ai, Mark Riedl, Ashwin Ram
19th International Conference on Case-Based Reasoning (ICCBR-11), London.
www.cc.gatech.edu/faculty/ashwin/papers/er-11-05.pdf
19
Jul
Posted by cognitivecomputing in Agents, Game AI, Learning, Talks, Web / Web 2.0. Tagged: believable agents, case-based reasoning, games, goal-driven learning, interactive drama, meta-reasoning, problem solving, real-time cbr, rts games, virtual worlds. 1 comment
(Click image to view the video – it’s near the bottom of the new page.)
User-generated content is everywhere: photos, videos, news, blogs, art, music, and every other type of digital media on the Social Web. Games are no exception. From strategy games to immersive virtual worlds, game players are increasingly engaged in creating and sharing nearly all aspects of the gaming experience: maps, quests, artifacts, avatars, clothing, even games themselves. Yet, there is one aspect of computer games that is not created and shared by game players: the AI. Building sophisticated personalities, behaviors, and strategies requires expertise in both AI and programming, and remains outside the purview of the end user.
To understand why authoring Game AI is hard, we need to understand how it works. AI can take digital entertainment beyond scripted interactions into the arena of truly interactive systems that are responsive, adaptive, and intelligent. I will discuss examples of AI techniques for character-level AI (in embedded NPCs, for example) and game-level AI (in the drama manager, for example). These types of AI enhance the player experience in different ways. The techniques are complicated and are usually implemented by expert game designers.
I propose an alternative approach to designing Game AI: Real-Time CBR. This approach extends CBR to real-time systems that operate asynchronously during game play, planning, adapting, and learning in an online manner. Originally developed for robotic control, Real-Time CBR can be used for interactive games ranging from multiplayer strategy games to interactive believable avatars in virtual worlds.
As with any CBR technique, Real-Time CBR integrates problem solving with learning. This property can be used to address the authoring problem. I will show the first Web 2.0 application that allows average users to create AIs and challenge their friends to play them—without programming. I conclude with some thoughts about the role of CBR in AI-based Interactive Digital Entertainment.
Keynote talk at the Eighteenth Conference on Pattern Recognition and Artificial Intelligence (RFIA-12), Lyon, France, February 5, 2012.
Slides and video here: rfia2012.liris.cnrs.fr/doku.php?id=pub:ram
Keynote talk at the Eleventh Scandinavian Conference on Artificial Intelligence (SCAI-11), Trondheim, Norway, May 25, 2011.
Keynote talk at the 2010 International Conference on Case-Based Reasoning (ICCBR-10), Alessandria, Italy, July 22, 2010.
GVU Brown Bag talk, October 14, 2010. Watch the talk here: www.gvu.gatech.edu/node/4320
Try it yourself:
Learn more about the algorithms:
View the talk:
www.sais.se/blog/?p=57
View the slides:
28
Sep
Posted by cognitivecomputing in Agents, Game AI, Learning, Talks, Web / Web 2.0. Tagged: believable agents, case-based reasoning, games, interactive drama, meta-reasoning, multistrategy learning, planning, problem solving, real-time cbr, rts games, virtual worlds. 3 comments
User-generated content is everywhere: photos, videos, news, blogs, art, music, and every other type of digital media on the Social Web. Games are no exception. From strategy games to immersive virtual worlds, game players are increasingly engaged in creating and sharing nearly all aspects of the gaming experience: maps, quests, artifacts, avatars, clothing, even games themselves. Yet, there is one aspect of computer games that is not created and shared by game players: the AI. Building sophisticated personalities, behaviors, and strategies requires expertise in both AI and programming, and remains outside the purview of the end user.
To understand why Game AI is hard, we need to understand how it works. AI can take digital entertainment beyond scripted interactions into the arena of truly interactive systems that are responsive, adaptive, and intelligent. I discuss examples of AI techniques for character-level AI (in embedded NPCs, for example) and game-level AI (in the drama manager, for example). These types of AI enhance the player experience in different ways. The techniques are complicated and are usually implemented by expert game designers.
I argue that User-Generated AI is the next big frontier in the rapidly growing Social Gaming area. From Sims to Risk to World of Warcraft, end users want to create, modify, and share not only the appearance but the “minds” of their characters. I present my recent research on intelligent technologies to assist Game AI authors, and show the first Web 2.0 application that allows average users to create AIs and challenge their friends to play them—without programming. I conclude with some thoughts about the future of AI-based Interactive Digital Entertainment.
CMU Robotics & Intelligence Seminar, September 28, 2009
Carnegie-Mellon University, Pittsburgh, PA.
MIT Media Lab Colloquium, January 25, 2010
Massachusetts Institute of Technology, Cambridge, MA.
Stanford Media X Philips Seminar, February 1, 2010
Stanford University, Stanford, CA.
Pixar Research Seminar, February 2, 2010
Try it yourself:
Learn more about the algorithms:
View the talk:
www.sais.se/blog/?p=57
View the slides:
19
Sep
Posted by cognitivecomputing in Game AI. Tagged: believable agents, case-based reasoning, games, interactive drama, problem solving. 2 comments
A growing research community is working towards employing drama management components in story-based games. These components gently guide the story towards a narrative arc that improves the player’s gaming experience. In this paper we evaluate a novel drama management approach deployed in an interactive fiction game called Anchorhead. This approach uses player’s feedback as the basis for guiding the personalization of the interaction.
The results indicate that adding our Case-based Drama manaGer (C-DraGer) to the game guides the players through the interaction and provides a better overall player experience. Unlike previous approaches to drama management, this paper focuses on exhibiting the success of our approach by evaluating results using human players in a real game implementation. Based on this work, we report several insights on drama management which were possible only due to an evaluation with real players.
Read the paper:
Drama Management and Player Modeling for Interactive Fiction Games
by Manu Sharma, Santi Ontañón, Manish Mehta, Ashwin Ram
Computational Intelligence, 26(2):183-211, 2010.
www.cc.gatech.edu/faculty/ashwin/papers/er-09-10.pdf
www3.interscience.wiley.com/journal/123387570/abstract
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