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Sravya Kondrakunta

dblp:195/8222 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2019
0009-0000-5738-4402ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Planning, search and constraint satisfaction · 50% Knowledge representation and reasoning · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning › cognitive modeling
cognitive architecture
0.312017
Goal Operations for Cognitive Systems · AAAI 2017
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
goal reasoning
0.312017
Goal Operations for Cognitive Systems · AAAI 2017

Methods — techniques the papers use, named apart from their topics

automated planning · 0.3
YearPublicationVenuePosition
2019 Probabilistic Selection of Case-Based Explanations in an Underwater Mine Clearance Domain
Venkatsampath Raja Gogineni, Sravya Kondrakunta, Danielle Brown, Matthew Molineaux, Michael T. Cox
ICCBR2
2017 Goal Operations for Cognitive Systems
abstract
Cognitive agents operating in complex and dynamic domains benefit from significant goal management. Operations on goals include formulation, selection, change, monitoring and delegation in addition to goal achievement. Here we model these operations as transformations on goals. An agent may observe events that affect the agent’s ability to achieve its goals. Hence goal transformations allow unachievable goals to be converted into similar achievable goals. This paper examines an implementation of goal change within a cognitive architecture. We introduce goal transformation at the metacognitive level as well as goal transformation in an automated planner and discuss the costs and benefits of each approach. We evaluate goal change in the MIDCA architecture using a resource-restricted planning domain, demonstrating a performance benefit due to goal operations.
Michael T. Cox, Dustin Dannenhauer, Sravya Kondrakunta
AAAI3