G. Sileno

Legal Knowledge Conveyed by Narratives: Towards a Representational Model

The paper investigates a representational model for narratives, aiming to facilitate the acquisition of the systematic core of stories concerning legal cases, i.e. the set of causal and temporal relationships that govern the world in which the narrated scenario takes place. At the discourse level, we consider narratives as sequences of messages collected in an observation, including descriptions of agents, of agents’ behaviour and of mechanisms relative to physical, mental and institutional domains. At the content level, stories correspond to synchronizations of embodied agent-roles scripts. Following this approach, the Pierson v Post case is analyzed in detail and represented as a Petri net. 1998 ACM Subject Classification H.1.2 Human information processing, I.2 Artificial Intelligence

Analysis of legal narratives: a conceptual framework

This article presents a conceptual framework intended to describe and to abstract cases or scenarios of compliance and non-compliance. These scenarios are collected in order to be animated in an agent-based platform for purposes of design and validation of both new regulations and new implementations, or to be used as reference base for a diagnosis tool. In our approach, legal narratives become a source of agent-roles descriptions, i.e. abstractions of individual characters/agents from singular stories, feeding the target applicative framework.

From Inter-agent to Intra-agent Representations

The paper introduces elements of a methodology for the acquisition of descriptions of social scenarios (e.g. cases) and for their synthesis to agent-based models. It proceeds along three steps. First, the case is analyzed at signal layer, i.e. the messages exchanged between actors. Second, the signal layer is enriched with intentions, internal actions, and necessary conditions for the story to occur. This elicitation is based on elements provided with the story, common-sense and expert knowledge, direct interaction with the narrator. Third, the resulting scenario representation is synthesized as agent programs. These scripts correspond to descriptions of agent-roles observed in that social setting.

Commitments, Expectations, Affordances and Susceptibilities: Towards Positional Agent Programming

The paper introduces an agent architecture centered around the notions of commitment, expectation, affordance, and susceptibility. These components are to a certain measure at the base of any agent system, however, inspired by research in explanation-based decision making, this contribution attempts to make explicit and start organizing under the same operationalization neglected figures as negative commitment, negative expectation, etc.

Reading agendas between the lines, an exercise

Abstract This work presents elements for an alternative operationalization of monitoring and diagnosis of multi-agent systems, developed in the context of compliance checking. In contrast to traditional accounts of model-based diagnosis, and most proposals concerning non-compliance, our method does not consider any commitment towards the individual unit of agency. Identity is considered to be mostly an attribute to assign responsibility, and not as the only referent to a source of intentionality. The proposed method requires as input a set of prototypical agent-roles known to be relevant for the domain, and an observation, i.e. evidence collected by a monitor agent. We elaborate on a concrete example concerning tax frauds in real-estate transactions.

Bridging Representations of Laws, of Implementations and of Behaviours

To align representations of law, of implementations of law and of concrete behaviours, we designed a common ground representational model for the three domains, based on the notion of position, building upon Petri nets. This paper reports on work to define subsumption between positional models.

A Constructivist Approach to Rule Bases

The paper presents a set of algorithms for the conversion of rule bases between priority-based and constraint-based representations. Inspired by research in precedential reasoning in law, such algorithms can be used for the analysis of a rule base, and for the study of the impact of the introduction of new rules. In addition, the paper explores an optimization mechanism, built upon assumptions about the world in which the rule-based system operates, providing a model of environmental adaptation. The investigation is relevant to practical reasoning, agent modeling and agent programming.

The institutional stance in agent-based simulations

This paper presents a multi-agent framework intended to animate scenarios of compliance and non-compliance in a normative system. With the purpose of describing social human behaviour, we choose to reduce social complexity by creating models of the involved agents starting from stories, and completing them with background theories derived from common-sense and expert knowledge. For this reason, we explore how an institutional perspective can be taken into account in a computational framework. Roles, institutions and rules become components of the agent architecture. The social intelligence of the agent is distributed to several cognitive modules, performing the institutional thinking, whose outcomes are coordinated in the main decision-making cycle. The institutional logic is analyzed from a general simulation perspective, and a concrete possible choice is presented, drawn from fundamental legal concepts. As a concrete result, a preliminary implementation of the framework has been developed with Jason.

Similarity and Contrast on Conceptual Spaces for Pertinent Description Generation

Within the general objective of conceiving a cognitive architecture for image interpretation able to generate outputs relevant to several target user profiles, the paper elaborates on a set of operations that should be provided by a cognitive space to guarantee the generation of relevant descriptions. First, it attempts to define a working definition of contrast operation. Then, revisiting well-known results in cognitive studies, it sketches a definition of similarity based on contrast, distinguished from the metric defined on the conceptual space.