Srivastava, Biplav

A Train Status Assistant for Indian Railways Artificial Intelligence

Trains are part-and-parcel of every day lives in countries with large, diverse, multi-lingual population like India. Consequently, an assistant which can accurately predict and explain train delays will help people and businesses alike. We present a novel conversation agent which can engage with people about train status and inform them about its delay at in-line stations. It is trained on past delay data from a subset of trains and generalizes to others.

Decision-support for the Masses by Enabling Conversations with Open Data Artificial Intelligence

Open data refers to data that is freely available for reuse. Although there has been rapid increase in availability of open data to public in the last decade, this has not translated into better decision-support tools for them. We propose intelligent conversation generators as a grand challenge that would automatically create data-driven conversation interfaces (CIs), also known as chatbots or dialog systems, from open data and deliver personalized analytical insights to users based on their contextual needs. Such generators will not only help bring Artificial Intelligence (AI)-based solutions for important societal problems to the masses but also advance AI by providing an integrative testbed for human-centric AI and filling gaps in the state-of-art towards this aim.

Towards Composable Bias Rating of AI Services Artificial Intelligence

A new wave of decision-support systems are being built today using AI services that draw insights from data (like text and video) and incorporate them in human-in-the-loop assistance. However, just as we expect humans to be ethical, the same expectation needs to be met by automated systems that increasingly get delegated to act on their behalf. A very important aspect of an ethical behavior is to avoid (intended, perceived, or accidental) bias. Bias occurs when the data distribution is not representative enough of the natural phenomenon one wants to model and reason about. The possibly biased behavior of a service is hard to detect and handle if the AI service is merely being used and not developed from scratch, since the training data set is not available. In this situation, we envisage a 3rd party rating agency that is independent of the API producer or consumer and has its own set of biased and unbiased data, with customizable distributions. We propose a 2-step rating approach that generates bias ratings signifying whether the AI service is unbiased compensating, data-sensitive biased, or biased. The approach also works on composite services. We implement it in the context of text translation and report interesting results.

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