Biplav Srivastava | IBM Research (original) (raw)

Papers by Biplav Srivastava

As computational power has continued to increase, and sensors have become more accurate, the corr... more As computational power has continued to increase, and sensors have become more accurate, the corresponding advent of systems that are at once cognitive and immersive has arrived. These cognitive and immersive systems (CAISs) fall squarely into the intersection of AI with HCI/HRI: such systems interact with and assist the human agents that enter them, in no small part because such systems are infused with AI able to understand and reason about these humans and their knowledge, beliefs, goals, communications, plans, etc. We herein explain our approach to engineering CAISs. We emphasize the capacity of a CAIS to develop and reason over a “theory of the mind” of its human partners. This capacity entails that the AI in question has a sophisticated model of the beliefs, knowledge, goals, desires, emotions, etc. of these humans. To accomplish this engineering, a formal framework of very high expressivity is needed. In our case, this framework is a cognitive event calculus, a particular kin...

Current AI systems lack several important human capabilities, such as adaptability, generalizabil... more Current AI systems lack several important human capabilities, such as adaptability, generalizability, self-control, consistency, common sense, and causal reasoning. We believe that existing cognitive theories of human decision making, such as the thinking fast and slow theory, can provide insights on how to advance AI systems towards some of these capabilities. In this paper, we propose a general architecture that is based on fast/slow solvers and a metacognitive component. We then present experimental results on the behavior of an instance of this architecture, for AI systems that make decisions about navigating in a constrained environment. We show how combining the fast and slow decision modalities allows the system to evolve over time and gradually pass from slow to fast thinking with enough experience, and that this greatly helps in decision quality, resource consumption, and efficiency.

ArXiv, 2021

Epistemic Planning (EP) refers to an automated planning setting where the agent reasons in the sp... more Epistemic Planning (EP) refers to an automated planning setting where the agent reasons in the space of knowledge states and tries to find a plan to reach a desirable state from the current state. Its general form, the Multi-agent Epistemic Planning (MEP) problem involves multiple agents who need to reason about both the state of the world and the information flow between agents. In a MEP problem, multiple approaches have been developed recently with varying restrictions, such as considering only the concept of knowledge while not allowing the idea of belief, or not allowing for “complex” modal operators such as those needed to handle dynamic common knowledge. While the diversity of approaches has led to a deeper understanding of the problem space, the lack of a standardized way to specify MEP problems independently of solution approaches has created difficulties in comparing performance of planners, identifying promising techniques, exploring new strategies like ensemble methods, a...

IEEE Internet Computing, 2021

2021 IEEE 9th International Conference on Healthcare Informatics (ICHI), 2021

During the ongoing COVID-19 crisis, subreddits on Reddit, such as r/Coronavirus saw a rapid growt... more During the ongoing COVID-19 crisis, subreddits on Reddit, such as r/Coronavirus saw a rapid growth in user’s requests for help (support seekers- SSs) including individuals with varying professions and experiences with diverse perspectives on care (support providers - SPs). Currently, knowledgeable human moderators match an SS with a user with relevant experience, i.e, an SP on these subreddits. This unscalable process defers timely care. We present a medical knowledge-infused approach to efficient matching of SS and SPs validated by experts for the users affected by anxiety and depression, in the context of with COVID-19. After matching, each SP to an SS labeled as either supportive, informative, or similar (sharing experiences) using the principles of natural language inference. Evaluation by 21 domain experts indicates the efficacy of incorporated knowledge and shows the efficacy the matching system.

Machine translation services are a very popular class of Artificial Intelligence (AI) services no... more Machine translation services are a very popular class of Artificial Intelligence (AI) services nowadays but public’s trust in these services is not guaranteed since they have been shown to have issues like bias. In this work, we focus on the behavior of machine translators with respect to gender bias as well as their accuracy. We have created the first-of-its-kind virtual environment, called VEGA, where the user can interactively explore translations services and compare their trust ratings using different visuals.

ArXiv, 2018

We briefly introduce herein a new form of distributed, multi-agent artificial intelligence, which... more We briefly introduce herein a new form of distributed, multi-agent artificial intelligence, which we refer to as "tentacular." Tentacular AI is distinguished by six attributes, which among other things entail a capacity for reasoning and planning based in highly expressive calculi (logics), and which enlists subsidiary agents across distances circumscribed only by the reach of one or more given networks.

2018 21st International Conference on Intelligent Transportation Systems (ITSC), 2018

India runs the fourth largest railway transport network size carrying over 8 billion passengers p... more 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 norder Markov frameworks and experiment with two regressionbased 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.

23rd International Conference on Intelligent User Interfaces, 2018

The emerging paradigm of dialogue interfaces for information retrieval systems opens new opportun... more The emerging paradigm of dialogue interfaces for information retrieval systems opens new opportunities for interactively narrowing down users' information query and improving search results. Prior research has largely focused on methods that use a set of close-ended questions, such as decision tree, to learn about the user's search target. However, when there is a myriad of documents or items to search, solely relying on close-ended questions can lead to long and undesirable dialogues. We propose an adaptive dialogue strategy framework that incorporates open-ended questions at the optimal timing to reduce the length of the dialogue. We propose a method to estimate the information gain of open-ended questions, and in each dialog turn, we compare it with that of close-ended questions to decide which question to ask. We present experiments using several synthetic datasets designed to explore the behavior of such an adaptive dialogue strategy under different environments, and compare the system's performance with that of a close-ended-questions-only strategy.

Interaction Design and Children, 2021

Developing the problem solving skills of children is a challenging problem that is crucial for th... more Developing the problem solving skills of children is a challenging problem that is crucial for the future of our society. Given that artificial intelligence (AI) has been used to solve problems across a wide variety of domains, AI offers unique opportunities to develop problem solving skills using a multitude of tasks that pique the curiosity of children. To make this a reality, it is necessary to address the uninterpretable "black-box" that AI often appears to be. Towards this goal, we design a collaborative artificial intelligence algorithm that uses a human-in-the-loop approach to allow students to discover their own personalized solutions to problems. This collaborative algorithm builds on state-of-the-art AI algorithms and leverages additional interpretable structures, namely knowledge graphs and decision trees, to create a fully interpretable process that is able to explain solutions in their entirety. We describe this algorithm when applied to solving the Rubik's cube as well as our planned user-interface and assessment methods. CCS CONCEPTS • Computing methodologies → Discrete space search; Sequential decision making; • Human-centered computing → Empirical studies in interaction design; Usability testing.

Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 2018

IEEE Internet Computing, 2018

IoT-Enhanced Human Experience The two articles in this special section represent ongoing Internet... more IoT-Enhanced Human Experience The two articles in this special section represent ongoing Internet of Things applications in the context of Europe trying to make solutions usable to people in daily times. Modern societies are seeing an unprecedented surge in the number and range of devices deployed and used in day-today applications, including mobile phones, tablets, wearable devices, and other connected sensing and actuation devices, collectively referred to as the Internet of Things (IoT). By one estimate, by 2020, 50 billion such devices are expected to be deployed. Such a massive number of IoT devices will be continuously or periodically making the data they generate available on the Internet. They represent an unprecedented opportunity to develop contextually intelligent applications with far-reaching societal implications. They can deliver fine-grained services in various areas such as health, fitness and wellbeing, manufacturing, transportation and logistics, disaster coordination, sustainability and the environment, and human development and social good. These intelligent applications and services, however, could also pose privacy, security, and trust issues and risk a person's safety. Consider a couple, Alex and Bela, living together for a period, taking care of themselves and each other's health as they proceed in their life from their early 20s to 70s. When they got married in the 1960s, they were getting annual physicals done by a family physician whose findings and vital readings were shared with them as paper reports. They kept some and lost some (possibly due to shifting homes over time). As they turned over 50 in the 1990s, and reports of some medical tests became digital, they started accessing them online at the portal or over email. Collating all these reports (paper and digital) to get a full health picture (for themselves or their doctor) meant printing all the reports on paper. If they changed insurance providers or doctors, they had to reproduce the full status so that the new providers could make accurate decisions without requiring new, unnecessary, and costly testing. Moving into their 70s in the 2010s, they have health data from annual checks as well as more periodic monitoring devices like fitness trackers (per second) and blood pressure monitors (daily). It has become very hard for them to put all their information together to show a doctor or review themselves. They rely on an insurance provider or a doctor network to do it, but as a side effect, they are now locked into a specific health network that has their digital data. The personalized data generated under their watch, also called patient-generated health data (PGHD), don't make it into the clinical system automatically, and the busy clinician may not may have time to look at the data on the patient's device, and may rarely incorporate the information gleaned from that data into clinical records. Furthermore,

AI Magazine, 2015

The AAAI-14 Workshop program was held Sunday and Monday, July 27–28, 2012, at the Québec City Con... more 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 Relati...

2016 IEEE International Conference on Services Computing (SCC), 2016

Simulation is known to be an effective technique to understand and manage traffic in cities of de... more Simulation is known to be an effective technique to understand and manage traffic in cities of developed countries. However, in developing countries, traffic management is lacking due to a wide diversity of vehicles on the road, their chaotic movement, little instrumentation to sense traffic state and limited funds to create IT and physical infrastructure to ameliorate the situation. Under these conditions, in this paper, we present our approach of using the Megaffic traffic simulator as a service to gain actionable insights for two use-cases and cities in India, a first. Our approach is general to be readily used in other use cases and cities, and our results give new insights: (a) using demographics data, traffic demand can be reduced if timings of government offices are altered in Delhi, (b) using a mobile company's Call Data Record (CDR) data to mine trajectories anonymously, one can take effective traffic actions while organizing events in Mumbai at local scale.

Proceedings of the Thirteenth National Conference on Artificial Intelligence Volume 2, Aug 4, 1996