Modelling the combination of functional and logic programming languages

The combination of functional and pure Horn clause logic languages is formally introduced. To give a framework for the investigation of implementations we define a complete and consistent model, which retains full invertibility and allows separation of logic and control. Some existing implementations are discussed from this viewpoint. An extended unification algorithm is suggested, which incorporates the features demanded by our model

Verifying RoboCup Teams

Pocreeding of: 5th International Workshop on Model Checking and Artificial Intelligence. MOCHART-2008, Patras, Greece, july, 21st, 2008.Verification of multi-agent systems is a challenging task due to their dynamic nature, and the complex interactions between agents. An example of such a system is the RoboCup Soccer Simulator, where two teams of eleven independent agents play a game of football against each other. In the present article we attempt to verify a number of properties of RoboCup football teams, using a methodology involving testing. To accomplish such testing in an efficient manner we use the McErlang model checker, as it affords precise control of the scheduling of the agents, and provides convenient access to the internal states and actions of the agents of the football teams.This work has been partially supported by the FP7-ICT-2007-1 project ProTest (215868), a Ramón y Cajal grant from the Spanish Ministerio de Educación y Ciencia, and the Spanish national projects TRA2007-67374-C02-02, TIN2006-15660-C02- 02 (DESAFIOS) and S-0505/TIC/0407 (PROMESAS).Publicad

Harnessing Higher-Order (Meta-)Logic to Represent and Reason with Complex Ethical Theories

The computer-mechanization of an ambitious explicit ethical theory, Gewirth's Principle of Generic Consistency, is used to showcase an approach for representing and reasoning with ethical theories exhibiting complex logical features like alethic and deontic modalities, indexicals, higher-order quantification, among others. Harnessing the high expressive power of Church's type theory as a meta-logic to semantically embed a combination of quantified non-classical logics, our work pushes existing boundaries in knowledge representation and reasoning. We demonstrate that intuitive encodings of complex ethical theories and their automation on the computer are no longer antipodes.Comment: 14 page

A Deontic Logic Reasoning Infrastructure

A flexible infrastructure for the automation of deontic and normative reasoning is presented. Our motivation is the development, study and provision of legal and moral reasoning competencies in future intelligent machines. Since there is no consensus on the “best” deontic logic formalisms and since the answer may be application specific, a flexible infrastructure is proposed in which candidate logic formalisms can be varied, assessed and compared in experimental ethics application studies. Our work thus links the historically rich research areas of classical higher-order logic, deontic logics, normative reasoning and formal ethics

Special issue on advances in first-order theorem proving - Foreword of the guest editors

The role of first-order theorem proving as a core theme of automated deduction was recognized since the beginning of the field, at the dawn of Artificial Intelligence, more than forty years ago. Although many other logics have been developed and used in AI, deduction systems based on first-order theorem proving recently have achieved considerable successes and even mention in the general press. It was a first-order theorem prover that first proved the Robbins algebra conjecture, and thus reached the New York Times Science section (NY Times, Dec. 10, 1996)

Diagnosis of Plan Execution and the Executing Agent

We adapt the Model-Based Diagnosis framework to perform (agent-based) plan diagnosis. In plan diagnosis, the system to be diagnosed is a plan, consisting of a partially ordered set of instances of actions, together with its executing agent. The execution of a plan can be monitored by making partial observations of the results of actions. Like in standard model-based diagnosis, observed deviations from the expected outcomes are explained qualifying some action instances that occur in the plan as behaving abnormally. Unlike in standard model-based diagnosis, however, in plan diagnosis we cannot assume that actions fail independently. We focus on two sources of dependencies between failures: dependencies that arise as a result of a malfunction of the executing agent, and dependencies that arise because of dependencies between action instances occurring in a plan. Therefore, we introduce causal rules that relate health states of the agent and health states of actions to abnormalities of other action instances. These rules enable us to introduce causal set and causal effect diagnoses that use the underlying causes of plan failing to explain deviations and to predict future anomalies in the execution of actions