Posts Tagged ‘planning’

Announcing the Sponsored Teams for the 2016-2017 Alexa Prize

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.


The Alexa Prize: $2.5M to Advance Conversational AI

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?


Learning from Demonstration to be a Good Team Member in a Role Playing Game

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).

Robust and Authorable Multiplayer Storytelling Experiences

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).

User-Generated AI for Interactive Digital Entertainment

CMU Seminar

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

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Run-Time Behavior Adaptation for Real-Time Interactive Games

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

Using Meta-Reasoning to Improve the Performance of Case-Based Planning

Case-based planning (CBP) systems are based on the idea of reusing past successful plans for solving new problems. Previous research has shown the ability of meta-reasoning approaches to improve the performance of CBP systems. In this paper we present a new meta-reasoning approach for autonomously improving the performance of CBP systems that operate in real-time domains.

Our approach uses failure patterns to detect anomalous behaviors, and it can learn from experience which of the failures detected are important enough to be fixed. Finally, our meta-reasoning approach can exploit both successful and failed executions for meta-reasoning.

We illustrate its benefits with experimental results from a system implementing our approach called Meta-Darmok in a real-time strategy game. The evaluation of Meta-Darmok shows that the system successfully adapts itself and its performance improves through appropriate revision of the case base.

Read the paper:

Using Meta-Reasoning to Improve the Performance of Case-Based Planning

by Manish Mehta, Santi Ontañón, Ashwin Ram

International Conference on Case-Based Reasoning (ICCBR-09), Seattle, July 2009