44 citations · 114 across the 29 of their papers we have counts for
17 papers · 1 filter
Doing Less with More: A First-Principles Exploration of the Suitability of Agentic Computing over Alternative Architectural Choices
Ritvik Garimella, Biplav Srivastava, Amit Sheth
There is growing interest in automating business activities with Agentic Artificial Intelligence (AI) due to latter's seeming ease of use. Never has it been easier, or costlier, to…
A Neurosymbolic Fast and Slow Architecture for Graph Coloring
Vedant Khandelwal, Vishal Pallagani, Biplav Srivastava +1
Constraint Satisfaction Problems (CSPs) present significant challenges to artificial intelligence due to their intricate constraints and the necessity for precise solutions. Existi…
The Case for Developing a Foundation Model for Planning-like Tasks from Scratch
Biplav Srivastava, Vishal Pallagani
Foundation Models (FMs) have revolutionized many areas of computing, including Automated Planning and Scheduling (APS). For example, a recent study found them useful for planning p…
On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS)
Vishal Pallagani, Kaushik Roy, Bharath Muppasani +7
Automated Planning and Scheduling is among the growing areas in Artificial Intelligence (AI) where mention of LLMs has gained popularity. Based on a comprehensive review of 126 pap…
On Solving the Rubik's Cube with Domain-Independent Planners Using Standard Representations
Bharath Muppasani, Vishal Pallagani, Biplav Srivastava +1
Rubik's Cube (RC) is a well-known and computationally challenging puzzle that has motivated AI researchers to explore efficient alternative representations and problem-solving meth…
Value-based Fast and Slow AI Nudging
Marianna B. Ganapini, Francesco Fabiano, Lior Horesh +7
Nudging is a behavioral strategy aimed at influencing people's thoughts and actions. Nudging techniques can be found in many situations in our daily lives, and these nudging techni…
A Planning Ontology to Represent and Exploit Planning Knowledge for Performance Efficiency
Bharath Muppasani, Vishal Pallagani, Biplav Srivastava +3
Ontologies are known for their ability to organize rich metadata, support the identification of novel insights via semantic queries, and promote reuse. In this paper, we consider t…
Understanding the Capabilities of Large Language Models for Automated Planning
Vishal Pallagani, Bharath Muppasani, Keerthiram Murugesan +5
Automated planning is concerned with developing efficient algorithms to generate plans or sequences of actions to achieve a specific goal in a given environment. Emerging Large Lan…
Towards Explainable and Safe Conversational Agents for Mental Health: A Survey
Surjodeep Sarkar, Manas Gaur, L. Chen +3
Virtual Mental Health Assistants (VMHAs) are seeing continual advancements to support the overburdened global healthcare system that gets 60 million primary care visits, and 6 mill…
Fast and Slow Planning
Francesco Fabiano, Vishal Pallagani, Marianna Bergamaschi Ganapini +5
The concept of Artificial Intelligence has gained a lot of attention over the last decade. In particular, AI-based tools have been employed in several scenarios and are, by now, pe…
Rating Sentiment Analysis Systems for Bias through a Causal Lens
Kausik Lakkaraju, Biplav Srivastava, Marco Valtorta
Sentiment Analysis Systems (SASs) are data-driven Artificial Intelligence (AI) systems that, given a piece of text, assign one or more numbers conveying the polarity and emotional…
Plansformer: Generating Symbolic Plans using Transformers
Vishal Pallagani, Bharath Muppasani, Keerthiram Murugesan +5
Large Language Models (LLMs) have been the subject of active research, significantly advancing the field of Natural Language Processing (NLP). From BERT to BLOOM, LLMs have surpass…
A Rich Recipe Representation as Plan to Support Expressive Multi Modal Queries on Recipe Content and Preparation Process
Vishal Pallagani, Priyadharsini Ramamurthy, Vedant Khandelwal +4
Food is not only a basic human necessity but also a key factor driving a society's health and economic well-being. As a result, the cooking domain is a popular use-case to demonstr…
Combining Fast and Slow Thinking for Human-like and Efficient Navigation in Constrained Environments
Marianna B. Ganapini, Murray Campbell, Francesco Fabiano +8
Current AI systems lack several important human capabilities, such as adaptability, generalizability, self-control, consistency, common sense, and causal reasoning. We believe that…
Thinking Fast and Slow in AI: the Role of Metacognition
Marianna Bergamaschi Ganapini, Murray Campbell, Francesco Fabiano +7
AI systems have seen dramatic advancement in recent years, bringing many applications that pervade our everyday life. However, we are still mostly seeing instances of narrow AI: ma…
Thinking Fast and Slow in AI
Grady Booch, Francesco Fabiano, Lior Horesh +8
This paper proposes a research direction to advance AI which draws inspiration from cognitive theories of human decision making. The premise is that if we gain insights about the c…
Planning with Partial Preference Models
Tuan Nguyen, Minh Do, Alfonso Gerevini +3
Current work in planning with preferences assume that the user's preference models are completely specified and aim to search for a single solution plan. In many real-world plannin…