Author name cluster

Pegah Alizadeh

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

2 author rows

ECAI Conference 2024 Conference Paper

Enhancing Decision-Making in Energy Management Systems Through Action-Independent Dynamics Learning

Théo Zangato
Aomar Osmani
Pegah Alizadeh

Incorporating auxiliary objectives into Reinforcement Learning allows agents to acquire additional knowledge, thereby increasing their search for the optimal policy. This article presents the Model-Predictor Proximal Policy Optimization (MP-PPO) algorithm, which merges the concepts of various PPO variants with a Transformer probabilistic prediction module. This model capitalizes on the time dependence inherent in energy management systems, predicting future state transitions by learning to predict certain state characteristics. Notably, our algorithm seamlessly integrates this predictive capability into the Actor-Critic architecture, avoiding the need for an external model. Through experiments on real data, we demonstrate that integrating predictive capabilities for partial state prediction improves both the sample effectiveness and efficiency of the original PPO approach without requiring exterior prior information.

Details

IJCAI Conference 2023 Conference Paper

Optimization-driven Demand Prediction Framework for Suburban Dynamic Demand-Responsive Transport Systems

Louis Zigrand
Roberto Wolfler Calvo
Emiliano Traversi
Pegah Alizadeh

Demand-Responsive Transport (DRT) has grown over the last decade as an ecological solution to both metropolitan and suburban areas. It provides a more efficient public transport service in metropolitan areas and satisfies the mobility needs in sparse and heterogeneous suburban areas. Traditionally, DRT operators build the plannings of their drivers by relying on myopic insertion heuristics that do not take into account the dynamic nature of such a service. We thus investigate in this work the potential of a Demand Prediction Framework used specifically to build more flexible routes within a Dynamic Dial-a-Ride Problem (DaRP) solver. We show how to obtain a Machine Learning forecasting model that is explicitly designed for optimization purposes. The prediction task is further complicated by the fact that the historical dataset is significantly sparse. We finally show how the predicted travel requests can be integrated within an optimization scheme in order to compute better plannings at the start of the day. Numerical results support the fact that, despite the data sparsity challenge as well as the optimization-driven constraints that result from the DaRP model, such a look-ahead approach can improve up to 3. 5% the average insertion rate of an actual DRT service.

PDF Details DOI

AAAI Conference 2022 Conference Paper

Clustering Approach to Solve Hierarchical Classification Problem Complexity

Aomar Osmani
Massinissa Hamidi
Pegah Alizadeh

In a large domain of classification problems for real applications, like human activity recognition, separable spaces between groups of concepts are easier to learn than each concept alone. This is because the search space biases required to separate groups of classes (or concepts) are more relevant than the ones needed to separate classes individually. For example, it is easier to learn the activities related to the body movements group (running, walking) versus ”on-wheels” activities group (bicycling, driving a car), before learning more specific classes inside each of these groups. Despite the obvious interest of this approach, our theoretical analysis shows a high complexity for finding an exact solution. We propose in this paper an original approach based on the association of clustering and classification approaches to overcome this limitation. We propose a better approach to learn the concepts by grouping classes recursively rather than learning them class by class. We introduce an effective greedy algorithm and two theoretical measures, namely cohesion and dispersion, to evaluate the connection between the clusters and the classes. Extensive experiments on the SHL dataset show that our approach improves classification performances while reducing the number of instances used to learn each concept.

PDF Details