Antonis C. Kakas

Argumentation based decision making for autonomous agents

This paper presents an argumentation based framework to support the decision making of an agent within a modular architecture for agents. The proposed argumentation framework is dynamic, with arguments and their strength depending on the particular context that the agent finds himself, thus allowing the agent to adapt his decisions in a changing environment. In addition, in order to enable the agent to operate within an open environment where the available information may be incomplete we have integrated abduction within this argumentation framework. This is particular useful when the agent finds himself in a dilemma and hence needs additional information to resolve this. We have also developed, motivated by work in Cognitive Psychology, within the same framework an argumentation based personality theory for agents thus incorporating a dimension of individuality in the decisions of the agent.

Computational Logic: Logic Programming and Beyond

In this paper we describe a distributed object oriented logic programming language in which an object is a collection of threads deductively accessing and updating a shared logic program. The key features of the language, such as static and dynamic object methods and multiple inheritance, are illustrated through a series of small examples. We show how we can implement object servers, allowing remote spawning of objects, which we can use as staging posts for mobile agents. We give as an example an information gathering mobile agent that can be queried about the information it has so far gathered whilst it is gathering new information. Finally we define a class of co-operative reasoning agents that can do resource bounded inference for full first order predicate logic, handling multiple queries and information updates concurrently. We believe that the combination of the concurrent OO and the LP programming paradigms produces a powerful tool for quickly implementing rational multi-agent applications on the internet.We present a new approach to termination analysis of logic programs. The essence of the approach is that we make use of general orderings (instead of level mappings), like it is done in transformational approaches to logic program termination analysis, but we apply these orderings directly to the logic program and not to the term-rewrite system obtained through some transformation. We define some variants of acceptability, based on general orderings, and show how they are equivalent to LD-termination. We develop a demand driven, constraint-based approach to verify these acceptability-variants. The advantage of the approach over standard acceptability is that in some cases, where complex level mappings are needed, fairly simple orderings may be easily generated. The advantage over transformational approaches is that it avoids the transformation step all together.

Abduction in Logic Programming

Abduction in Logic Programming started in the late 80s, early 90s, in an attempt to extend logic programming into a framework suitable for a variety of problems in Artificial Intelligence and other areas of Computer Science. This paper aims to chart out the main developments of the field over the last ten years and to take a critical view of these developments from several perspectives: logical, epistemological, computational and suitability to application. The paper attempts to expose some of the challenges and prospects for the further development of the field.

ACLP: Abductive Constraint Logic Programming

Abstract This paper presents the framework of Abductive Constraint Logic Programming (ACLP), which integrates Abductive Logic Programming (ALP) and Constraint Logic Programming (CLP). In ACLP, the task of abduction is supported and enhanced by its non-trivial integration with constraint solving. This integration of constraint solving into abductive reasoning facilitates a general form of constructive abduction and enables the application of abduction to computationally demanding problems. The paper studies the formal declarative and operational semantics of the ACLP framework together with its application to various problems. The general characteristics of the computation of ACLP and of its application to problems are also discussed. Empirical results based on an implementation of the ACLP framework on top of the CLP language of ECLiPSe show that ACLP is computationally viable, with performance comparable to the underlying CLP framework on which it is built. In addition, our experiments show the natural ability for ACLP to accommodate easily and in a robust way new or changing requirements of the original problem. ACLP thus combines the advantages of modularity and flexibility of the high-level representation afforded by abduction together with the computational effectiveness of low-level specialised constraint solving.

Application of abductive ILP to learning metabolic network inhibition from temporal data

In this paper we use a logic-based representation and a combination of Abduction and Induction to model inhibition in metabolic networks. In general, the integration of abduction and induction is required when the following two conditions hold. Firstly, the given background knowledge is incomplete. Secondly, the problem must require the learning of general rules in the circumstance in which the hypothesis language is disjoint from the observation language. Both these conditions hold in the application considered in this paper. Inhibition is very important from the therapeutic point of view since many substances designed to be used as drugs can have an inhibitory effect on other enzymes. Any system able to predict the inhibitory effect of substances on the metabolic network would therefore be very useful in assessing the potential harmful side-effects of drugs. In modelling the phenomenon of inhibition in metabolic networks, background knowledge is used which describes the network topology and functional classes of inhibitors and enzymes. This background knowledge, which represents the present state of understanding, is incomplete. In order to overcome this incompleteness hypotheses are considered which consist of a mixture of specific inhibitions of enzymes (ground facts) together with general (non-ground) rules which predict classes of enzymes likely to be inhibited by the toxin. The foreground examples are derived from in vivo experiments involving NMR analysis of time-varying metabolite concentrations in rat urine following injections of toxins. The model’s performance is evaluated on training and test sets randomly generated from a real metabolic network. It is shown that even in the case where the hypotheses are restricted to be ground, the predictive accuracy increases with the number of training examples and in all cases exceeds the default (majority class). Experimental results also suggest that when sufficient training data is provided, non-ground hypotheses show a better predictive accuracy than ground hypotheses. The model is also evaluated in terms of the biological insight that it provides.

A simple declarative language for describing narratives with actions

Abstract We describe a simple declarative languageEfor describing the effects of a series of action occurrences within a narrative.Eis analogous to Gelfond and Lifschitzs LanguageAand its extensions, but is based on a different ontology. The semantics ofEis based on a simple characterisation of persistence which facilitates a modular approach to extending the expressivity of the language. Domain descriptions inAcan be translated to equivalent theories inE. We show how, in the context of reasoning about actions,Es narrative-based ontology may be exploited in order to characterise and synthesise two complementary notions of explanation. According to the first notion, explanation may be partly modelled as the process of suitably extending an apparently inconsistent theory written inEso as to establish consistency, thus providing a natural method, in many cases, to account for conflicting sets of information about the domain. According to the second notion, observations made at later times can sometimes be explained in terms of what is true at earlier times. This enables domains to be given an alternative characterisation in which knowledge arising from observations is appropriately separated from other aspects of the domain. We also describe howEdomains may be implemented as Event Calculus style logic programs, which facilitate automated reasoning both backwards and forwards in time, and which behave correctly even when the knowledge entailed by the domain description is incomplete.

Adaptive agent negotiation via argumentation

In this paper, we study how argumentation can be used as a basis for negotiation between autonomous agents, where negotiation strategies of the different parties are represented as argumentation theories within their knowledge. We propose an argumentation based negotiation protocol in which offers by the negotiating parties are linked to different arguments that they can build according to their individual negotiation strategy. This protocol is able to take into account the different roles of agents and context of interaction, where the strength of the arguments supporting an offer can depend on these factors. The agents can adapt their negotiation strategies and offers, as their environment changes, in particular during the course of the negotiation as they exchange information. In addition, using abduction alongside with argumentation, agents can find negotiating conditions to support an argument for an offer, thus extending the negotiation object in order to help find an agreement. To illustrate further the advantages of the approach we also study how we can capture and extend negotiation strategies within another negotiation mechanism, in particular the well-known mechanism of bargaining with multiple parties. This exposes further the main advantages of modularity and flexibility of our argumentation based representation of negotiation strategies and the adaptability that this offers in a changing environment of negotiation.

Using argumentation logic for firewall policy specification and analysis

Firewalls are important perimeter security mechanisms that imple-ment an organisations network security requirements and can be notoriously difficult to configure correctly. Given their widespread use, it is crucial that network administrators have tools to translate their security requirements into firewall configuration rules and ensure that these rules are consistent with each other. In this paper we propose an approach to firewall policy specification and analysis that uses a formal framework for argumentation based preference reasoning. By allowing administrators to define network abstractions (e.g. subnets, protocols etc) security requirements can be specified in a declarative manner using high-level terms. Also it is possible to specify preferences to express the importance of one requirement over another. The use of a formal framework means that the security requirements defined can be automatically analysed for inconsistencies and firewall configurations can be automatically generated. We demonstrate that the technique allows any inconsistency property, including those identified in previous research, to be specified and automatically checked and the use of an argumentation reasoning framework provides administrators with information regarding the causes of the inconsistency.

Layered strategies and protocols for argumentation-based agent interaction

Communication between agents needs to be flexible enough to encompass together a variety of different aspects such as, conformance to society protocols, private tactics of the individual agents, strategies that reflect different classes of agent types (or personal attitudes) and adaptability to the particular external circumstances at the time when the communication takes place. In this paper we propose an argument-based framework for representing communication theories of agents that can take into account in a uniform way these different aspects. We show how this approach can be used to realize existing types of dialogue strategies and society protocols in a way that facilitates their modular development and extension to make them more flexible in handling different or special circumstances.

Abductive and inductive reasoning: background and issues

The outline of this chapter is as follows. In Section 1.2 we discuss the philosophical and logical origins of abduction and induction. In Section 1.3 we analyse previous work on abduction and induction in the context of logic programming\indexlogic programming and artificial intelligence, and attempt a (partial) synthesis of this work. Section 1.4 considers the integration of abduction and induction in artificial intelligence, and Section 1.5 concludes.