Posts Tagged ‘planning’
31
Dec
Posted by cognitivecomputing in Agents, Language, Learning, Opinion, Talks. Tagged: believable agents, goal-driven learning, information retrieval, Learning, meta-reasoning, natural language, personality models, planning, problem solving, semantic memory, social learning, virtual worlds. Leave a comment
Ashwin Ram works on the AI behind Alexa, one of several new bots that might change the way your home and your life function within the next few years. Imagine a bot that turns on your lights, shops for you, even helps you make decisions. Learn more about a bot-enabled future that might have you saying (like Shah Rukh Khan does): “Alexa, I love you!”
TALK & TRANSCRIPT:
#TomorrowsWorld made easier with Artificial Intelligence. #TEDTalksIndiaNayiSoch
ted.com/talks/ashwin_ram_could_bots_make_your_life_better
BEHIND THE SCENES:
Innovator and entrepreneur, Ashwin Ram believes AI will changes our lives in future. #TomorrowsWorld #TEDTalksIndiaNayiSoch
youtube.com/watch?v=kDvIsRuaq5k
FULL #TOMORROWSWORLD EPISODE:
Can you imagine what #TomorrowsWorld will be like? Shah Rukh Khan introduces.
tedtalksindianayisoch.hotstar.com/TED/episode-4.php
ALL TED TALKS INDIA NAYI SOCH:
#TEDTalksIndiaNayiSoch is a groundbreaking TV series showcasing new thinking from some of the brightest brains in India and beyond and hosted by “The King of Bollywood,” Shah Rukh Khan.
ted.com/series/ted_talks_india_nayi_soch
28
Nov
Posted by cognitivecomputing in Agents, Language, Learning, Talks. Tagged: believable agents, cognitive media, creativity, information retrieval, Learning, multistrategy learning, natural language, personality models, planning, problem solving, semantic memory, social learning, text cbr. Leave a comment
Conversational agents are exploding in popularity. However, much work remains in the area of social conversation as well as free-form conversation over a broad range of domains and topics. To advance the state of the art in conversational AI, Amazon launched the Alexa Prize, a 2.5-million-dollar university competition where sixteen selected university teams were challenged to build conversational agents, known as “socialbots”, to converse coherently and engagingly with humans on popular topics such as Sports, Politics, Entertainment, Fashion and Technology for 20 minutes.
The Alexa Prize offered the academic community a unique opportunity to perform research with a live system used by millions of users. The competition provided university teams with real user conversational data at scale, along with the user-provided ratings and feedback augmented with annotations by the Alexa team. This enabled teams to effectively iterate and make improvements throughout the competition while being evaluated in real-time through live user interactions.
To build their socialbots, university teams combined state-of-the-art techniques with novel strategies in the areas of Natural Language Understanding, Context Modeling, Dialog Management, Response Generation, and Knowledge Acquisition. To support the teams’ efforts, the Alexa Prize team made significant scientific and engineering investments to build and improve Conversational Speech Recognition, Topic Tracking, Dialog Evaluation, Voice User Experience, and tools for traffic management and scalability.
This paper outlines the advances created by the university teams as well as the Alexa Prize team to achieve the common goal of solving the problem of Conversational AI.
Conversational AI: The Science behind the Alexa Prize
by Ashwin Ram, Rohit Prasad, Chandra Khatri, Anu Venkatesh, Raefer Gabriel, Qing Liu, Jeff Nunn, Behnam Hedayatnia, Ming Cheng, Ashish Nagar, Eric King, Kate Bland, Amanda Wartick, Yi Pan, Han Song, Sk Jayadevan, Gene Hwang, Art Pettigrue
Proceedings of the 2017 Alexa Prize
Invited talk at NIPS-2017 Workshop on Conversational AI
Invited talk at re:Invent 2017 (with Spyros Matsoukas)
READ THE PAPER:
arxiv.org/abs/1801.03604
WATCH THE TALK:
youtu.be/pn5QJQZjGpM
29
Aug
Posted by cognitivecomputing in Agents, Language, Learning, Opinion, Web / Web 2.0. Tagged: believable agents, cognitive media, creativity, information retrieval, Learning, meta-reasoning, natural language, personality models, planning, problem solving, semantic memory. Leave a comment
We’ve hit another milestone in the Alexa Prize, a $2.5 million university competition to advance conversational AI. University teams from around the world have been hard at work to create a socialbot, an AI capable of conversing coherently and engagingly with humans on popular topics and news events for 20 minutes.
I am now excited to announce the university teams that will be competing in the finals! After hundreds of thousands of conversations, the two socialbots with the highest average customer ratings during the semifinal period are Alquist from the Czech Technical University in Prague and Sounding Board from the University of Washington in Seattle. The wildcard team is What’s Up Bot from Heriot-Watt University in Edinburgh, Scotland.
READ MORE:
developer.amazon.com/blogs/alexa/post/783df492-4770-4b11-81ac-59e009669d56/announcing-the-2017-alexa-prize-finalists
18
May
Posted by cognitivecomputing in Agents, Language, Learning, Opinion, Web / Web 2.0. Tagged: believable agents, cognitive media, creativity, information retrieval, 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. In April, university teams from around the world assembled at the appropriately named Day 1 building in Seattle for the Alexa Prize Summit. The event was a base camp for teams to share learnings and make preparations for the most challenging leg of their journey: to build and scale an AI capable of conversing coherently and engagingly with humans for 20 minutes.
As they build their “socialbots,” they will encounter esoteric problems like context modeling and dialog planning as well as exoteric problems like user experience and conversational engagement. And they will need all the help they can get.
We invite you to join the students on their journey and help them along the way. You can interact with their socialbots simply by saying, “Alexa, let’s chat” on any device with Alexa.
READ MORE:
developer.amazon.com/blogs/alexa/post/e4cc64d1-f334-4d2d-8609-5627939f9bf7/join-the-alexa-prize-journey-and-test-the-socialbots
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
24
May
Posted by cognitivecomputing in Agents, Game AI. Tagged: believable agents, case-based reasoning, games, meta-reasoning, planning, rts games. Leave a comment
We present an approach that uses learning from demonstration in a computer role playing game. We describe a behavior engine that uses case-based reasoning. The behavior engine accepts observation traces of human playing decisions and produces a sequence of actions which can then be carried out by an artificial agent within the gaming environment. Our work focuses on team-based role playing games, where the agents produced by the behavior engine act as team members within a mixed human-agent team. We present the results of a study we conducted, where we assess both the quantitative and qualitative performance difference between human-only teams compared with hybrid human-agent teams.
Learning from Demonstration to be a Good Team Member in a Role Playing Game
by Michael Silva, Silas McCroskey, Jonathan Rubin, Michael Youngblood, Ashwin Ram
26th International FLAIRS Conference on Artificial Intelligence (FLAIRS-13).
www.cc.gatech.edu/faculty/ashwin/papers/er-13-01.pdf
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
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:
9
Sep
Posted by cognitivecomputing in Game AI, Learning. Tagged: believable agents, games, goal-driven learning, meta-reasoning, planning, real-time cbr, virtual worlds. Leave a comment
Intelligent agents working in real-time domains need to adapt to changing circumstance so that they can improve their performance and avoid their mistakes. AI agents designed for interactive games, however, typically lack this ability. Game agents are traditionally implemented using static, hand-authored behaviors or scripts that are brittle to changing world dynamics and cause a break in player experience when they repeatedly fail. Furthermore, their static nature causes a lot of effort for the game designers as they have to think of all imaginable circumstances that can be encountered by the agent. The problem is exacerbated as state-of-the-art computer games have huge decision spaces, interactive user input, and real-time performance that make the problem of creating AI approaches for these domains harder.
In this paper we address the issue of non-adaptivity of game playing agents in complex real-time domains. The agents carry out run-time adaptation of their behavior sets by monitoring and reasoning about their behavior execution to dynamically carry out revisions on the behaviors. The behavior adaptation approaches has been instantiated in two real-time interactive game domains. The evaluation results shows that the agents in the two domains successfully adapt themselves by revising their behavior sets appropriately.
Read the paper:
Run-Time Behavior Adaptation for Real-Time Interactive Games
by Manish Mehta, Ashwin Ram
IEEE Transactions on Computational Intelligence and AI in Games, Vol. 1, No. 3, September 2009
www.cc.gatech.edu/faculty/ashwin/papers/er-09-09.pdf
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