Chitta Baral

Arizona State University
https://www.public.asu.edu/~cbaral/
Publications329
h-index40
Citations7,600
Highly Influential Citations553
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Knowledge Representation, Reasoning and Declarative Problem Solving
TLDR
Chitta Baral demonstrates how to write programs that behave intelligently by giving them the ability to express knowledge and reason about it and presents a language, AnsProlog, for both knowledge representation and reasoning, and declarative problem solving.
Probabilistic reasoning with answer sets
TLDR
A declarative language, P-log, that combines logical and probabilistic arguments in its reasoning is developed and it is argued that the approach to updates is more appealing than existing approaches.
Logic Programming and Knowledge Representation
Knowledge Representation, Reasoning and Declarative Problem Solving: Query answering and answer set computing systems
Towards Addressing the Winograd Schema Challenge - Building and Using a Semantic Parser and a Knowledge Hunting Module
TLDR
This paper presents an approach that identifies the knowledge needed to answer a challenge question, hunts down that knowledge from text repositories, and then reasons with machines to come up with the answer.
Formalizing sensing actions A transition function based approach
MUTANT: A Training Paradigm for Out-of-Distribution Generalization in Visual Question Answering
TLDR
This work presents MUTANT, a training paradigm that exposes the model to perceptually similar, yet semantically distinct mutations of the input, to improve OOD generalization, such as the VQA-CP challenge.
Combining Multiple Knowledge Bases
TLDR
The authors define the concept of maximality and prove that the algorithms presented combine the knowledge bases to generate a maximal theory and discuss the relationships between combining multiple knowledge bases and the view update problem.
Discovering drug–drug interactions: a text-mining and reasoning approach based on properties of drug metabolism
TLDR
A novel approach that integrates text mining and automated reasoning to derive DDIs is proposed that can uncover potential DDIs with scientific evidences explaining the mechanism of the interactions.
What to do and how to do it: Translating natural language directives into temporal and dynamic logic representation for goal management and action execution
TLDR
An integrated robotic architecture is described that can achieve the above steps by translating natural language instructions incrementally and simultaneously into formal logical goal description and action languages, which can be used both to reason about the achievability of a goal as well as to generate new action scripts to pursue the goal.
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