Posts Tagged ‘case-based reasoning’

Augmented Social Cognition for Consumer Health and Wellness

In a recent Wall Street Journal essay, Marc Andreessen wrote: “Software is eating the world. Over the next 10 years, I expect many more industries to be disrupted by software. Healthcare and education are next up for fundamental software-based transformation.”

What is the impending disruption in healthcare, and what new technologies are driving it? I argue that the problem is not healthcare but health: creating new consumer-centric approaches to health and wellness that increase engagement, improve health literacy and promote behavior change.

The web is evolving from information (portals) to interaction (social/mobile) to influence: shaping attitudes and behaviors. This creates a unique opportunity to address the problem of consumer health and wellness. But, to do this effectively requires a new kind of technology: user modeling. It also requires an innovation methodology that is fundamentally about people, not technology.

At PARC, our research in Augmented Social Cognition is centered around the confluence of three technologies: social, mobile, and user modeling. I discuss these technologies and explain how we leverage artificial Intelligence (AI) and case-based reasoning (CBR) techniques to model users and create effective and sustainable behavior change.

Invited talk at CBR-2013 Industry Day, Saratoga Springs, NY, July 8, 2013.
VIEW SLIDES:

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).
www.cc.gatech.edu/faculty/ashwin/papers/er-13-01.pdf

Socio-Semantic Conversational Information Access

We develop an innovative approach to delivering relevant information using a combination of socio-semantic search and filtering approaches. The goal is to facilitate timely and relevant information access through the medium of conversations by mixing past community specific conversational knowledge and web information access to recommend and connect users and information together. Conversational Information Access is a socio-semantic search and recommendation activity with the goal to interactively engage people in conversations by receiving agent supported recommendations. It is useful because people engage in online social discussions unlike solitary search; the agent brings in relevant information as well as identifies relevant users; participants provide feedback during the conversation that the agent uses to improve its recommendations.

Socio-Semantic Conversational Information Access

by Saurav Sahay, Ashwin Ram

WWW-2012 Workshop on Community Question Answering on the Web (CQA-12).

Socio-Semantic Conversational Information Access

The main contributions of this thesis revolve around development of an integrated conversational recommendation system, combining data and information models with community network and interactions to leverage multi-modal information access. We have developed a real time conversational information access community agent that leverages community knowledge by pushing relevant recommendations to users of the community. The recommendations are delivered in the form of web resources, past conversation and people to connect to. The information agent (cobot, for community/ collaborative bot) monitors the community conversations, and is ‘aware’ of users’ preferences by implicitly capturing their short term and long term knowledge models from conversations. The agent leverages from health and medical domain knowledge to extract concepts, associations and relationships between concepts; formulates queries for semantic search and provides socio-semantic recommendations in the conversation after applying various relevance filters to the candidate results. The agent also takes into account users’ verbal intentions in conversations while making recommendation decision.

One of the goals of this thesis is to develop an innovative approach to delivering relevant information using a combination of social networking, information aggregation, semantic search and recommendation techniques. The idea is to facilitate timely and relevant social information access by mixing past community specific conversational knowledge and web information access to recommend and connect users with relevant information. Language and interaction creates usable memories, useful for making decisions about what actions to take and what information to retain.

Cobot leverages these interactions to maintain users’ episodic and long term semantic models. The agent analyzes these memory structures to match and recommend users in conversations by matching with the contextual information need. The social feedback on the recommendations is registered in the system for the algorithms to promote community preferred, contextually relevant resources. The nodes of the semantic memory are frequent concepts extracted from user’s interactions. The concepts are connected with associations that develop when concepts co-occur frequently. Over a period of time when the user participates in more interactions, new concepts are added to the semantic memory. Different conversational facets are matched with episodic memories and a spreading activation search on the semantic net is performed for generating the top candidate user recommendations for the conversation.

The tying themes in this thesis revolve around informational and social aspects of a unified information access architecture that integrates semantic extraction and indexing with user modeling and recommendations.

Read the dissertation:

Socio-Semantic Conversational Information Access

by Saurav Sahay

PhD dissertation, College of Computing, Georgia Institute of Technology, November 2011.

smartech.gatech.edu/handle/1853/42855

Case-Based Reasoning Research And Development

This book constitutes the refereed proceedings of the 19th International Conference on Case-Based Reasoning, held in London, UK, in September 2011. The 32 contributions presented together with 3 invited talks were carefully reviewd and selected from 67 submissions. The presentations and posters covered a wide range of CBR topics of interest both to practitioners and researchers, including CBR methodology covering case representation, similarity, retrieval, and adaptation; provenance and maintenance; recommender systems; multi-agent collaborative systems; data mining; time series analysis; Web applications; knowledge management; legal reasoning; healthcare systems and planning systems.
Find the book:

Case-Based Reasoning Research and Development | Lecture Notes in Artificial Intelligence, Vol. 6880

edited by Ashwin Ram and Nirmalie Wiratunga

Springer, October 20, 2011, ISBN 978-3-642-23290-9
www.springer.com/computer/ai/book/978-3-642-23290-9

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).
www.cc.gatech.edu/faculty/ashwin/papers/er-11-06.pdf

Construction and Adaptation of AI Behaviors in Computer Games

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

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