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Lower Troncoso Member is one of the most important reservoirs within the Neuquen Basin, Argentina, where it hosts large amounts of reserves.

Increasing road traffic levels in urban areas require actions and policies to manage and control the number of road users. Travelers’ choices of transport modes, particularly private cars, that generate the main share of road traffic levels, depend on many factors, which include both personal preferences and level-of-service variables. Understanding how travelers choose transport modes according to the above factors is an important challenge in order to adopt the most suitable policies and facilitate a sustainable mobility. In the literature, behavioral models have been mainly proposed in order to both estimate mode choice percentages and capture travel behaviors by suitable estimation of some parameters associated to the above factors. However, behavior is complex in itself and the mechanisms underlying user behavior might be difficult to be captured by traditional models. In this paper, a neuro-fuzzy approach is proposed to extract mode choice decision rules by evaluating different sets of rules and different membership functions of the neuro-fuzzy model. Particularly, to determine which inputs are the most relevant in such decision process, fuzzy curves and surfaces have been considered in order to take into account nonlinear effects. The neuro-fuzzy model proposed in this paper has been thought to be embedded in an agent-based methodological framework where user agents – representing travelers – make travel choices based on the rules learnt by means of the neuro-fuzzy system.

In this paper we sketch a vision of computable law as argumentation-based MAS, i.e., human-centred intelligent systems densely populated by agents (software or human) capable of understanding, arguing, and reporting, via factual assertions and arguments, about what is happening and what they can make possibly happen. A multi-agent system based on argumentation, dialogue, and conversation is, in this vision, the basis for making the law computable: through argumentation, dialogue, and adherence to social judgment, the behaviour of the intelligent system can be reached, shaped and controlled with respect to the law. In such a scenario, computable law – and related intelligent behaviour – is likely to become associated with the capability of arguing about situations and about the current state and situation, by reaching a consensus on what is happening around and what is needed, and by triggering and orchestrating proper decentralised semantic conversations to decide how to collectively act in order to reach a future desirable state. Interpretability and explainability become important features of such a system based on the integration of logic-based and sub-symbolic techniques. Within this novel setting, MAS methodologies and technologies become the starting point to achieve computable law, even if they need to be adapted and extended for dealing with arising challenges. Accordingly, in this paper, we discuss how such a novel vision can build upon some readily available technologies, and the research challenges it poses. We analyse a number of approaches and technologies that should be involved in the engineering of systems and services and should have to become core expertise for distributed systems engineering. Among the others, these include knowledge representation, machine learning, logic argumentation.

Mainstream programming languages nowadays tends to be more and more multi-paradigm ones, by integrating diverse programming paradigms—e.g., object-oriented programming (OOP) and functional programming (FP). Logic-programming (LP) is a successful paradigm that has contributed to many relevant results in the areas of symbolic AI and multi-agent systems, among the others. Whereas Prolog, the most successful LP language, is typically integrated with mainstream languages via foreign language interfaces, in this paper we propose an alternative approach based on the notion of domain-specific language (DSL), which makes LP available to OOP programmers straightforwardly within their OO language of choice. In particular, we present a Kotlin DSL for Prolog, showing how the Kotlin multi-paradigm (OOP + FP) language can be enriched with LP in a straightforward and effective way. Since it is based on the interoperable 2P-Kt project, our technique also enables the creation of similar DSL on top of other high-level languages such as Scala or JavaScript—thus paving the way towards a more general adoption of LP in general-purpose programming environments.

Preface to the Proceedings of the 21st Workshop “From Objects to Agents”

The idea of integrating symbolic and sub-symbolic approaches to make intelligent systems (IS) understandable and explainable is at the core of new fields such as neuro-symbolic computing (NSC). This work lays under the umbrella of NSC, and aims at a twofold objective. First, we present a set of guidelines aimed at building explainable IS, which leverage on logic induction and constraints to integrate symbolic and sub-symbolic approaches. Then, we reify the proposed guidelines into a case study to show their effectiveness and potential, presenting a prototype built on the top of some NSC technologies.