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Found 20 papers, 0 papers with code
On the Sample Efficiency of Abstractions and Potential-Based Reward Shaping in Reinforcement Learning
The use of Potential Based Reward Shaping (PBRS) has shown great promise in the ongoing research effort to tackle sample inefficiency in Reinforcement Learning (RL).
Computer User Interface Understanding. A New Dataset and a Learning Framework
We also present a framework that is composed of a synthetic sample generation pipeline to augment the dataset with relevant characteristics, and a contrastive learning method to classify images in the videos.
Methods for Matching English Language Addresses
Addresses occupy a niche location within the landscape of textual data, due to the positional importance carried by every word, and the geographical scope it refers to.
On Computing Plans with Uniform Action Costs
In many real-world planning applications, agents might be interested in finding plans whose actions have costs that are as uniform as possible.
Generalising Planning Environment Redesign
Most research on planning environment (re)design assumes the interested party's objective is to facilitate the recognition of goals and plans, and search over the space of environment modifications to find the minimal set of changes that simplify those tasks and optimise a particular metric.
Synthetic Data Applications in Finance
Synthetic data has made tremendous strides in various commercial settings including finance, healthcare, and virtual reality.
Classification of Tabular Data by Text Processing
Natural Language Processing technology has advanced vastly in the past decade.
Contrastive Explanations of Centralized Multi-agent Optimization Solutions
We propose CMAoE, a domain-independent approach to obtain contrastive explanations by: (i) generating a new solution $S^\prime$ where property $P$ is enforced, while also minimizing the differences between $S$ and $S^\prime$; and (ii) highlighting the differences between the two solutions, with respect to the features of the objective function of the multi-agent system.
A Look into Causal Effects under Entangled Treatment in Graphs: Investigating the Impact of Contact on MRSA Infection
In this paper, we study the problem of causal effect estimation with treatment entangled in a graph.
Fairness in Multi-Agent Planning
In cooperative Multi-Agent Planning (MAP), a set of goals has to be achieved by a set of agents.
Inapplicable Actions Learning for Knowledge Transfer in Reinforcement Learning
Knowing this information can help reduce the sample complexity of RL algorithms by masking the inapplicable actions from the policy distribution to only explore actions relevant to finding an optimal policy.
Anticipatory Counterplanning
In competitive environments, commonly agents try to prevent opponents from achieving their goals.
Advising Agent for Service-Providing Live-Chat Operators
Call centers, in which human operators attend clients using textual chat, are very common in modern e-commerce.
Simulating and classifying behavior in adversarial environments based on action-state traces: an application to money laundering
One of the key characteristics of these applications is the wide range of strategies that an adversary may choose as they adapt their strategy dynamically to sustain benefits and evade authorities.
Goal recognition via model-based and model-free techniques
In this paper, we adapt state-of-the-art learning techniques to goal recognition, and compare model-based and model-free approaches in different domains.
Domain-independent generation and classification of behavior traces
Financial institutions mostly deal with people.
Guarantees for Sound Abstractions for Generalized Planning (Extended Paper)
Recently it has been shown how to reduce the planning problem for generalized planning to the planning problem for a qualitative numerical problem; the latter being a reformulation that simultaneously captures all the instances in the collection.
Error Analysis and Correction for Weighted A*'s Suboptimality (Extended Version)
There is broad consensus that this bound is not very accurate, that the actual suboptimality of wA*'s solution is often much less than W times optimal.
Scaling up Heuristic Planning with Relational Decision Trees
Current evaluation functions for heuristic planning are expensive to compute.
