Search Results for author:
Found 13 papers, 0 papers with code
On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS)
Automated Planning and Scheduling is among the growing areas in Artificial Intelligence (AI) where mention of LLMs has gained popularity.
Proceedings 39th International Conference on Logic Programming
This volume contains the Technical Communications presented at the 39th International Conference on Logic Programming (ICLP 2023), held at Imperial College London, UK from July 9 to July 15, 2023.
Value-based Fast and Slow AI Nudging
Values that are relevant to a specific decision scenario are used to decide when and how to use each of these nudging modalities.
Understanding the Capabilities of Large Language Models for Automated Planning
Firstly, we want to understand the extent to which LLMs can be used for plan generation.
Fast and Slow Planning
The concept of Artificial Intelligence has gained a lot of attention over the last decade.
Plansformer: Generating Symbolic Plans using Transformers
Large Language Models (LLMs) have been the subject of active research, significantly advancing the field of Natural Language Processing (NLP).
Combining Fast and Slow Thinking for Human-like and Efficient Navigation in Constrained Environments
Current AI systems lack several important human capabilities, such as adaptability, generalizability, self-control, consistency, common sense, and causal reasoning.
Thinking Fast and Slow in AI: the Role of Metacognition
AI systems have seen dramatic advancement in recent years, bringing many applications that pervade our everyday life.
Comprehensive Multi-Agent Epistemic Planning
In particular, this manuscript is focused on a specialized kind of planning known as Multi-agent Epistemic Planning (MEP).
E-PDDL: A Standardized Way of Defining Epistemic Planning Problems
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.
Thinking Fast and Slow in AI
This paper proposes a research direction to advance AI which draws inspiration from cognitive theories of human decision making.
Modelling Multi-Agent Epistemic Planning in ASP. Theory and Practice of Logic Programming
In these scenarios, epistemic reasoning, i. e., reasoning about agents' beliefs about themselves and about other agents' beliefs, is essential to design winning strategies.
Design of a Solver for Multi-Agent Epistemic Planning
This type of reasoning, i. e., about agents' perception of the world and also about agents' knowledge of her and others' knowledge, is referred to as epistemic reasoning.
