The CIFF Proof Procedure for Abductive Logic Programming with Constraints: Theory, Implementation and Experiments

We present the CIFF proof procedure for abductive logic programming with constraints, and we prove its correctness. CIFF is an extension of the IFF proof procedure for abductive logic programming, relaxing the original restrictions over variable quantification (allowedness conditions) and incorporating a constraint solver to deal with numerical constraints as in constraint logic programming. Finally, we describe the CIFF system, comparing it with state of the art abductive systems and answer set solvers and showing how to use it to program some applications. (To appear in Theory and Practice of Logic Programming - TPLP)

CRAFTING THE MIND OF PROSOCS AGENTS

PROSOCS agents are software agents that are built according to the KGP model of agency. KGP is used as a model for the mind of the agent, so that the agent can act autonomously using a collection of logic theories, providing the mind's reasoning functionalities. The behavior of the agent is controlled by a cycle theory that specifies the agent's preferred patterns of operation. The implementation of the mind's generic functionality in PROSOCS is worked out in such a way so it can be instantiated by the platform for different agents across applications. In this context, the development of a concrete example illustrates how an agent developer might program the generic functionality of the mind for a simple application. 20 2-4 105 131 Cited By :1

Goal Reasoning: Papers from the ACS workshop

This technical report contains the 11 accepted papers presented at the Workshop on Goal Reasoning, which was held as part of the 2013 Conference on Advances in Cognitive Systems (ACS-13) in Baltimore, Maryland on 14 December 2013. This is the third in a series of workshops related to this topic, the first of which was the AAAI-10 Workshop on Goal-Directed Autonomy while the second was the Self-Motivated Agents (SeMoA) Workshop, held at Lehigh University in November 2012. Our objective for holding this meeting was to encourage researchers to share information on the study, development, integration, evaluation, and application of techniques related to goal reasoning, which concerns the ability of an intelligent agent to reason about, formulate, select, and manage its goals/objectives. Goal reasoning differs from frameworks in which agents are told what goals to achieve, and possibly how goals can be decomposed into subgoals, but not how to dynamically and autonomously decide what goals they should pursue. This constraint can be limiting for agents that solve tasks in complex environments when it is not feasible to manually engineer/encode complete knowledge of what goal(s) should be pursued for every conceivable state. Yet, in such environments, states can be reached in which actions can fail, opportunities can arise, and events can otherwise take place that strongly motivate changing the goal(s) that the agent is currently trying to achieve. This topic is not new; researchers in several areas have studied goal reasoning (e.g., in the context of cognitive architectures, automated planning, game AI, and robotics). However, it has infrequently been the focus of intensive study, and (to our knowledge) no other series of meetings has focused specifically on goal reasoning. As shown in these papers, providing an agent with the ability to reason about its goals can increase performance measures for some tasks. Recent advances in hardware and software platforms (involving the availability of interesting/complex simulators or databases) have increasingly permitted the application of intelligent agents to tasks that involve partially observable and dynamically-updated states (e.g., due to unpredictable exogenous events), stochastic actions, multiple (cooperating, neutral, or adversarial) agents, and other complexities. Thus, this is an appropriate time to foster dialogue among researchers with interests in goal reasoning. Research on goal reasoning is still in its early stages; no mature application of it yet exists (e.g., for controlling autonomous unmanned vehicles or in a deployed decision aid). However, it appears to have a bright future. For example, leaders in the automated planning community have specifically acknowledged that goal reasoning has a prominent role among intelligent agents that act on their own plans, and it is gathering increasing attention from roboticists and cognitive systems researchers. In addition to a survey, the papers in this workshop relate to, among other topics, cognitive architectures and models, environment modeling, game AI, machine learning, meta-reasoning, planning, selfmotivated systems, simulation, and vehicle control. The authors discuss a wide range of issues pertaining to goal reasoning, including representations and reasoning methods for dynamically revising goal priorities. We hope that readers will find that this theme for enhancing agent autonomy to be appealing and relevant to their own interests, and that these papers will spur further investigations on this important yet (mostly) understudied topic

The CIFF Proof Procedure for Abductive Logic Programming with Constraints: Definition, Implementation and a Web Application

Abduction has found broad application as a powerful tool for hypothetical reasoning with incomplete knowledge, which can be handled by labeling some pieces of information as abducibles, i.e. as possible hypotheses that can be assumed to hold, provided that they are consistent with the given knowledge base. Attempts to make the abductive reasoning an effective computational tool gave rise to Abductive Logic Programming (ALP) which combines abduction with standard logic programming. A number of so-called proof procedures for ALP have been proposed in the literature, e.g. the IFF procedure, the Kakas and Mancarella procedure and the SLDNFA procedure, which rely upon extensions of different semantics for logic programming. ALP has also been integrated with Constraint Logic Programming (CLP), in order to combine abductive reasoning with an arithmetic tool for constraint solving. In recent years, many proof procedures for abductive logic programming with constraints have been proposed, including ACLP and the A-System which have been applied to many fields, e.g. multi-agent systems, scheduling, integration of information. This dissertation describes the development of a new abductive proof procedure with constraints, namely the CIFF proof procedure. The description is both at the theoretical level, giving a formal definition and a soundness result with respect to the three-valued completion semantics, and at the implementative level with the implemented CIFF System 4.0 as a Prolog meta-interpreter. The main contributions of the CIFF proof procedure are the advances in the expressiveness of the framework with respect to other frameworks for abductive logic programming with constraints, and the overall computational performances of the implemented system. The second part of the dissertation presents a novel application of the CIFF proof procedure as the computational engine of a tool, the CIFFWEB system, for checking and (possibly) repairing faulty web sites. Indeed, the exponential growth of the WWW raises the question of maintaining and automatically repairing web sites, in particular when the designers of these sites require them to exhibit certain properties at both structural and data level. The capability of maintaining and repairing web sites is also important to ensure the success of the Semantic Web vision. As the Semantic Web relies upon the definition and the maintenance of consistent data schemas (XML/XMLSchema, RDF/RDFSchema, OWL and so on), tools for reasoning over such schemas (and possibly extending the reasoning to multiple web pages) show great promise. The CIFFWEB system is such a tool which allows to verify and to repair XML web sites instances, against sets of requirements which have to be fulfilled, through abductive reasoning. We define an expressive characterization of rules for checking and repairing web sites' errors and we do a formal mapping of a fragment of a well known XML query language, namely Xcerpt, to abductive logic programs suitable to fed as input to the CIFF proof procedure. Finally, the CIFF proof procedure detects the errors and possibly suggests modifications to the XML instances to repair them. The soundness of this process is directly inherited from the soundness of CIFF

Planning partially for situated agents

In recent years, within the planning literature there has been a departure from approaches computing total plans for given goals, in favour of approaches computing partial plans. Total plans can be seen as (partially ordered) sets of actions which, if executed successfully, would lead to the achievement of the goals. Partial plans, instead, can be seen as (partially ordered) sets of actions which, if executed successfully, would contribute to the achievement of the goals, subject to the achievement of further sub-goals. Planning partially (namely computing partial plans for goals) is useful (or even necessary) for a number of reasons: (i) because the planning agent is resource-bounded, (ii) because the agent has incomplete and possibly incorrect knowledge of the environment in which it is situated, (iii) because this environment is highly dynamic. In this paper, we propose a framework to design situated agents capable of planning partially. The framework is based upon the specification of planning problems via an abductive variant of the event calculus