Demo Data from EDBT 2026 / reviewers in the wild / expert
Sravya Kondrakunta
dblp:195/8222
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Knowledge representation and reasoning › cognitive modeling
cognitive architecture |
0.3 | 1 | 2017 | Goal Operations for Cognitive Systems · AAAI 2017 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
goal reasoning |
0.3 | 1 | 2017 | Goal Operations for Cognitive Systems · AAAI 2017 |
Methods — techniques the papers use, named apart from their topics
automated planning · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 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 |
ICCBR | 2 |
| 2017 | Goal Operations for Cognitive SystemsabstractCognitive 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 |
AAAI | 3 |