Counterfactuals as modal conditionals, and their probability
In this paper we propose a semantic analysis of Lewis' counterfactuals. By exploiting the structural properties of the recently introduced boolean algebras of conditionals, we show that counterfactuals can be expressed as formal ...
Explainable acceptance in probabilistic and incomplete abstract argumentation frameworks
Dung's Argumentation Framework (AF) has been extended in several directions, including the possibility of representing uncertainty about the existence of arguments and attacks. In this regard, two main proposals have been introduced in ...
Automatic generation of dominance breaking nogoods for a class of constraint optimization problems
Constraint Optimization Problems (COPs) ask for an assignment of values to variables in order to optimize an objective subject to constraints that restrict the value combinations in the assignment. They are usually solved by the ...
Fast and accurate data-driven goal recognition using process mining techniques
The problem of goal recognition requests to automatically infer an accurate probability distribution over possible goals an autonomous agent is attempting to achieve in the environment. The state-of-the-art approaches for goal ...
The notion of Abstraction in Ontology-based Data Management
We study a novel reasoning task in Ontology-based Data Management (OBDM), called Abstraction, which aims at associating formal semantic descriptions to data services. In OBDM a domain ontology is used to provide a semantic layer mapped ...
Approximability and efficient algorithms for constrained fixed-horizon POMDPs with durative actions
Partially Observable Markov Decision Process (POMDP) is a fundamental model for probabilistic planning in stochastic domains. More recently, constrained POMDP and chance-constrained POMDP extend the model allowing constraints to be ...
Multi-modal graph contrastive encoding for neural machine translation
As an important extension of conventional text-only neural machine translation (NMT), multi-modal neural machine translation (MNMT) aims to translate input source sentences paired with images into the target language. Although a lot of ...
Learning reward machines: A study in partially observable reinforcement learning
- Rodrigo Toro Icarte,
- Toryn Q. Klassen,
- Richard Valenzano,
- Margarita P. Castro,
- Ethan Waldie,
- Sheila A. McIlraith
Reinforcement Learning (RL) is a machine learning paradigm wherein an artificial agent interacts with an environment with the purpose of learning behaviour that maximizes the expected cumulative reward it receives from the environment. ...
A conflict-directed approach to chance-constrained mixed logical linear programming
Resistance to the adoption of autonomous systems comes in part from the perceived unreliability of the systems. The concerns can be addressed by deploying decision making algorithms that defines what it means to fail, and look for ...
Increasing revenue in Bayesian posted price auctions through signaling
We study single-item single-unit Bayesian posted price auctions, where buyers arrive sequentially and their valuations for the item being sold depend on a random, unknown state of nature. The seller has complete knowledge of the actual ...
Sequential model-based diagnosis by systematic search
Model-based diagnosis aims at identifying the real cause of a system's malfunction based on a formal system model and observations of the system behavior. To discriminate between multiple fault hypotheses (diagnoses), ...
Budget-feasible mechanisms for proportionally selecting agents from groups
In many social domains involving collective decision-making (e.g., committee selection and survey sampling), it is often desirable to select individuals from different population groups to achieve proportional representation (e.g., to ...
