Posts Tagged ‘semantic memory’

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?


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.

CBArch: A Case-Based Reasoning Framework for Conceptual Design of Commercial Buildings

The paper describes the first phase of development of a Case-Base Reasoning (CBR) system to support early conceptual design of buildings. As specific context of application, the research focuses on energy performance of commercial buildings, and the early identification of energy-related features that contribute to its outcomes. The hypothesis is that bringing knowledge from relevant precedents may facilitate this identification process, thus offering a significant contribution for early analysis and decision-making.

The paper introduces a proof-of-concept for such a system, proposing a novel integration of Case-Based Reasoning, Parametric Modeling (Building Information Modeling), and Ontology Classification. While CBR provides a framework to store and retrieve cases at an instance level, Parametric Modeling offers a framework for rule-based geometric adaptation and evaluation. The ontology is intended to provide a semantic representation, so that new design concepts can be created, classified and retained for further reuse. Potential advantages and limitations of this three-level integration approach are discussed along with recommendations for future development.

CBArch: A Case-Based Reasoning Framework for Conceptual Design of Commercial Buildings

by Andrés Cavieres, Urjit Bhatia, Preetam Joshi, Fei Zhao, Ashwin Ram

AAAI-11 Spring Symposium on Artificial Intelligence and Sustainable Design

Conversational Framework for Web Search and Recommendations

We introduce a Conversational Interaction framework as an innovative and natural approach to facilitate easier information access by combining web search and recommendations. This framework includes an intelligent information agent (Cobot) in the conversation to provide contextually relevant social and web search recommendations. Cobot supports the information discovery process by integrating web information retrieval along with proactive connections to relevant users who can participate in real-time conversations. We describe the conversational framework and report on some preliminary experiments in the system.

Read the paper:

Conversational Framework for Web Search and Recommendations

by Saurav Sahay, Ashwin Ram

ICCBR-10 Workshop on Reasoning from Experiences on the Web (WebCBR-10), Alessandria, Italy, 2010.

Collaborative Information Access: A Conversational Search Approach

Knowledge and user-generated content is proliferating on the web in scientific publications, information portals and online social media. This knowledge explosion has continued to outpace technological innovation in efficient information access technologies. In this paper, we describe methods and technologies for “Conversational Search” as an innovative solution to facilitate easier information access and reduce the information overload for users.

Conversational Search is an interactive and collaborative information finding interaction. The participants in this interaction engage in social conversations aided with an intelligent information agent (Cobot) that provides contextually relevant search recommendations. The collaborative and conversational search activity helps users make faster and more informed search and discovery. It also helps the agent learn about conversations with interactions and social feedback to make better recommendations. Conversational search leverages the social discovery process by integrating web information retrieval along with the social interactions.

Read the paper:

Collaborative Information Access: A Conversational Search Approach

by Saurav Sahay, Anu Venkatesh, Ashwin Ram

ICCBR-09 Workshop on Reasoning from Experiences on the Web (WebCBR-09), Seattle, July 2009

On Similarity Measures based on a Refinement Lattice

Retrieval of structured cases using similarity has been studied in CBR but there has been less activity on defining similarity on description logics (DL). We present an approach that allows us to present two similarity measures for feature logics, a subfamily of DLs, based on the concept of “refinement lattice”. The first one is based on computing the anti-unification (AU) of two cases to assess the amount of shared information. The second measure decomposes the cases into a set of independent “properties”, and then assesses how many of these properties are shared between the two cases. Moreover, we show that the defined measures are applicable to any representation language for which a refinement lattice can be defined. We empirically evaluate our measures comparing them to other measures in the literature in a variety of relational data sets showing very good results.

Read the paper:

On Similarity Measures based on a Refinement Lattice

by Santi Ontañón and Enric Plaza

in ICCBR 2009, LNAI 5650, pp 240 – 255