Srivastava, Biplav


Estimating Train Delays in a Large Rail Network Using a Zero Shot Markov Model

arXiv.org Machine Learning

India runs the fourth largest railway transport network size carrying over 8 billion passengers per year. However, the travel experience of passengers is frequently marked by delays, i.e., late arrival of trains at stations, causing inconvenience. In a first, we study the systemic delays in train arrivals using n-order Markov frameworks and experiment with two regression based models. Using train running-status data collected for two years, we report on an efficient algorithm for estimating delays at railway stations with near accurate results. This work can help railways to manage their resources, while also helping passengers and businesses served by them to efficiently plan their activities.


On Chatbots Exhibiting Goal-Directed Autonomy in Dynamic Environments

AAAI Conferences

Conversation interfaces (CIs), or chatbots, are a popular form of intelligent agents that engage humans in taskoriented or informal conversation. In this position paper and demonstration, we argue that chatbots working in dynamic environments, like with sensor data, can not only serve as a promising platform to research issues at the intersection of learning, reasoning, representation and execution for goal-directed autonomy; but also handle non-trivial business applications. We explore the underlying issues in the context of Water Advisor, a preliminary multi-modal conversation system that can access and explain water quality data.


A Cognitive Assistant for Visualizing and Analyzing Exoplanets

AAAI Conferences

We demonstrate an embodied cognitive agent that helps scientists visualize and analyze exo-planets and their host stars. The prototype is situated in a room equipped with a large display, microphones, cameras, speakers, and pointing devices. Users communicate with the agent via speech, gestures, and combinations thereof, and it responds by displaying content and generating synthesized speech. Extensive use of context facilitates natural interaction with the agent.


Water Advisor - A Data-Driven, Multi-Modal, Contextual Assistant to Help With Water Usage Decisions

AAAI Conferences

We demonstrate Water Advisor, a multi-modal assistant to help non-experts make sense of complex water quality data and apply it to their specific needs. A user can chat with the tool about water quality and activities of interest, and the system tries to advise using available water data for a location, applicable water regulations and relevant parameters using AI methods.


Mr. Jones — Towards a Proactive Smart Room Orchestrator

AAAI Conferences

In this brief abstract we report work in progress on developing Mr.Jones — a proactive orchestrator and decision support agent for a collaborative decision making setting embodied by a smart room. The duties of such an agent may range across interactive problem solving with other agents in the environment, developing automated summaries of meetings, visualization of the internal decision making process, proactive data and resource management, and so on. Specifically, we highlight the importance of integrating higher level symbolic reasoning and intent recognition in the design of such an agent, and outline pathways towards the realization of these capabilities.We will demonstrate some of these functionalities here in the context of automated orchestration of a meeting in the CEL — the Cognitive Environments Laboratory at IBM's T. J. Watson Research Center.


Reports of the Workshops of the Thirty-First AAAI Conference on Artificial Intelligence

AI Magazine

Reports of the Workshops of the Thirty-First AAAI Conference on Artificial Intelligence


Automatically Augmenting Titles of Research Papers for Better Discovery

AAAI Conferences

It is well known that the title of an article impacts how well it is discovered by potential readers and read. With both people and search engines, acting on behalf of people, accessing papers from digital libraries, it is important that the paper titles should promote discovery. In this paper, we investigate the characteristics of titles of AI papers and then propose au- tomatic ways to augment them so that they can be better in- dexed and discovered by users. A user study with researchers shows that they overwhelmingly prefer the augmented titles over the originals for being more helpful.


Reports of the AAAI 2014 Conference Workshops

AI Magazine

The AAAI-14 Workshop program was held Sunday and Monday, July 27–28, 2012, at the Québec City Convention Centre in Québec, Canada. The AAAI-14 workshop program included fifteen workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were AI and Robotics; Artificial Intelligence Applied to Assistive Technologies and Smart Environments; Cognitive Computing for Augmented Human Intelligence; Computer Poker and Imperfect Information; Discovery Informatics; Incentives and Trust in Electronic Communities; Intelligent Cinematography and Editing; Machine Learning for Interactive Systems: Bridging the Gap between Perception, Action and Communication; Modern Artificial Intelligence for Health Analytics; Multiagent Interaction without Prior Coordination; Multidisciplinary Workshop on Advances in Preference Handling; Semantic Cities -- Beyond Open Data to Models, Standards and Reasoning; Sequential Decision Making with Big Data; Statistical Relational AI; and The World Wide Web and Public Health Intelligence. This article presents short summaries of those events.


Reports of the AAAI 2014 Conference Workshops

AI Magazine

The AAAI-14 Workshop program was held Sunday and Monday, July 27–28, 2012, at the Québec City Convention Centre in Québec, Canada. Canada. The AAAI-14 workshop program included fifteen workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were AI and Robotics; Artificial Intelligence Applied to Assistive Technologies and Smart Environments; Cognitive Computing for Augmented Human Intelligence; Computer Poker and Imperfect Information; Discovery Informatics; Incentives and Trust in Electronic Communities; Intelligent Cinematography and Editing; Machine Learning for Interactive Systems: Bridging the Gap between Perception, Action and Communication; Modern Artificial Intelligence for Health Analytics; Multiagent Interaction without Prior Coordination; Multidisciplinary Workshop on Advances in Preference Handling; Semantic Cities — Beyond Open Data to Models, Standards and Reasoning; Sequential Decision Making with Big Data; Statistical Relational AI; and The World Wide Web and Public Health Intelligence. This article presents short summaries of those events.


The D-SCRIBE Process for Building a Scalable Ontology

AAAI Conferences

In this paper, we describe the D-SCRIBE process used to build ontologies that are expected to have significant domain expansion after their initial introduction and whose coverage of concepts needs to be validated for a series of related applications. This process has been used to build SCRIBE, a very modular, ambitious ontology for the information about events triggered by both humans or nature, response activities by agencies that provide public services in cities by using resources and assets (land parcels, buildings, vehicles, equipment) and their communication (requests, work orders, sensor reports). SCRIBE reuses concepts from previously existing ontologies and data exchange standards, and D-SCRIBE retains traceability to these source influences.