A neural cognitive model of argumentation with application to legal inference and decision making

Formal models of argumentation have been investigated in several areas, from multi-agent systems and artificial intelligence (AI) to decision making, philosophy and law. In artificial intelligence, logic-based models have been the standard for the representation of argumentative reasoning. More recently, the standard logic-based models have been shown equivalent to standard connectionist models. This has created a new line of research where (i) neural networks can be used as a parallel computational model for argumentation and (ii) neural networks can be used to combine argumentation, quantitative reasoning and statistical learning. At the same time, non-standard logic models of argumentation started to emerge. In this paper, we propose a connectionist cognitive model of argumentation that accounts for both standard and non-standard forms of argumentation. The model is shown to be an adequate framework for dealing with standard and non-standard argumentation, including joint-attacks, argument support, ordered attacks, disjunctive attacks, meta-level attacks, self-defeating attacks, argument accrual and uncertainty. We show that the neural cognitive approach offers an adequate way of modelling all of these different aspects of argumentation. We have applied the framework to the modelling of a public prosecution charging decision as part of a real legal decision making case study containing many of the above aspects of argumentation. The results show that the model can be a useful tool in the analysis of legal decision making, including the analysis of what-if questions and the analysis of alternative conclusions. The approach opens up two new perspectives in the short-term: the use of neural networks for computing prevailing arguments efficiently through the propagation in parallel of neuronal activations, and the use of the same networks to evolve the structure of the argumentation network through learning (e.g. to learn the strength of arguments from data)

Unimodal late fusion for NIST i-vector challenge on speaker detection

Speaker detection is a very interesting machine learning task for which the latest i-vector challenge has been coordinated by the National Institute of Standards and Technology (NIST). A simple late fusion approach for the speaker detection task on the i-vector challenge is presented. The approach is based on the late fusion of scores from the cosine distance method (the baseline) and the scores obtained from linear discriminant analysis. The results show that by adapting the simple late fusion approach, the framework can outperform the baseline score for the decision cost function on the NIST i-vector machine learning challenge

On the Relationship between I-O Logic and Connectionism

Abstract In this paper we present an embedding of (a fragment of) Input/Output logic into feed forward Neural Networks. We make use of the neural-symbolic methodology in order to allow neural networks to reason about normative systems. By doing so we are able to exploit normative reasoning within the neural networks setting. We aim at showing how neural networks can be used to represent a knowledge base of Input/Ouput logic rules and to reason about dilemma and contrary to duty problems

Neural-symbolic cognitive agents: architecture, theory and application

In real-world applications, the eective integration of learn- ing and reasoning in a cognitive agent model is a dicult task. However, such integration may lead to a better under- standing, use and construction of more realistic multiagent models. Existing models are either oversimplied or require too much processing time, which is unsuitable for online learning and reasoning. In particular, higher-order concepts and cognitive abilities have many unknown temporal rela- tions with the data, making it impossible to represent such relationships by hand. In this paper, we develop and apply a Neural-Symbolic Cognitive Agent (NSCA) model for online learning and reasoning that seeks to eectively represent, learn and reason in complex real-world applications

Neural-symbolic cognitive agents: architecture, theory and application

In real-world applications, the eective integration of learn- ing and reasoning in a cognitive agent model is a dicult task. However, such integration may lead to a better under- standing, use and construction of more realistic multiagent models. Existing models are either oversimplied or require too much processing time, which is unsuitable for online learning and reasoning. In particular, higher-order concepts and cognitive abilities have many unknown temporal rela- tions with the data, making it impossible to represent such relationships by hand. In this paper, we develop and apply a Neural-Symbolic Cognitive Agent (NSCA) model for online learning and reasoning that seeks to eectively represent, learn and reason in complex real-world applications

Applying Neural-Symbolic Cognitive Agents in Intelligent Transport Systems to reduce CO2 emissions

Providing personalized feedback in Intelligent Transport Systems is a powerful tool for instigating a change in driving behaviour and the reduction of CO2 emissions. This requires a system that is capable of detecting driver characteristics from real-time vehicle data. In this paper, we apply the architecture and theory of a Neural-Symbolic Cognitive Agent (NSCA) to effectively learn and reason about observed driving behaviour and related driver characteristics. The NSCA architecture combines neural learning and reasoning with symbolic temporal knowledge representation and is capable of encoding background knowledge, learning new hypotheses from observed data, and inferring new beliefs based on these hypotheses. Furthermore, it deals with uncertainty and errors in the data using a Bayesian inference model, and it scales well to hundreds of thousands of data samples as in the application reported in this paper. We have applied the NSCA in an Intelligent Transport System to reduce CO2 emissions as part of an European Union project, called EcoDriver. Results reported in this paper show that the NSCA outperforms the state-of-the-art in this application area, and is applicable to very large data