Harmonization of conflicting medical opinions using argumentation protocols and textual entailment - a case study on Parkinson disease

Parkinson's disease is the second most common neurodegenerative disease, affecting more than 1.2 million people in Europe. Medications are available for the management of its symptoms, but the exact cause of the disease is unknown and there is currently no cure on the market. To better understand the relations between new findings and current medical knowledge, we need tools able to analyse published medical papers based on natural language processing and tools capable to identify various relationships of new findings with the current medical knowledge. Our work aims to fill the above technological gap. To identify conflicting information in medical documents, we enact textual entailment technology. To encapsulate existing medical knowledge, we rely on ontologies. To connect the formal axioms in ontologies with natural text in medical articles, we exploit ontology verbalisation techniques. To assess the level of disagreement between human agents with respect to a medical issue, we rely on fuzzy aggregation. To harmonize this disagreement, we design mediation protocols within a multi-agent framework.Comment: ICCP 201

Agreeing on Defeasible Commitments

Abstract. Social commitments are developed for multi-agent systems according to the current practice in law regarding contract formation and breach. Deafeasible commitments are used to provide a useful link between multi-agent systems and legal doctrines. The proposed model makes the commitments more expressive relative to contract law, improving the model for the life cycle of the commitments. As a consequence, the broader semantics helps in modelling different types of contracts: gratuitous promises, unilateral contracts, bilateral contracts, and forward contracts. The semantics of higher-order commitments is useful in deciding whether to sign an agreement or not, due to a larger variety of protocols and contracts.