4,578 research outputs found
Logic-Based Specification Languages for Intelligent Software Agents
The research field of Agent-Oriented Software Engineering (AOSE) aims to find
abstractions, languages, methodologies and toolkits for modeling, verifying,
validating and prototyping complex applications conceptualized as Multiagent
Systems (MASs). A very lively research sub-field studies how formal methods can
be used for AOSE. This paper presents a detailed survey of six logic-based
executable agent specification languages that have been chosen for their
potential to be integrated in our ARPEGGIO project, an open framework for
specifying and prototyping a MAS. The six languages are ConGoLog, Agent-0, the
IMPACT agent programming language, DyLog, Concurrent METATEM and Ehhf. For each
executable language, the logic foundations are described and an example of use
is shown. A comparison of the six languages and a survey of similar approaches
complete the paper, together with considerations of the advantages of using
logic-based languages in MAS modeling and prototyping.Comment: 67 pages, 1 table, 1 figure. Accepted for publication by the Journal
"Theory and Practice of Logic Programming", volume 4, Maurice Bruynooghe
Editor-in-Chie
Distributed Abductive Reasoning: Theory, Implementation and Application
Abductive reasoning is a powerful logic inference mechanism that allows assumptions to be
made during answer computation for a query, and thus is suitable for reasoning over incomplete
knowledge. Multi-agent hypothetical reasoning is the application of abduction in a distributed
setting, where each computational agent has its local knowledge representing partial world and
the union of all agents' knowledge is still incomplete. It is different from simple distributed
query processing because the assumptions made by the agents must also be consistent with
global constraints.
Multi-agent hypothetical reasoning has many potential applications, such as collaborative planning
and scheduling, distributed diagnosis and cognitive perception. Many of these applications
require the representation of arithmetic constraints in their problem specifications as well as
constraint satisfaction support during the computation. In addition, some applications may
have confidentiality concerns as restrictions on the information that can be exchanged between
the agents during their collaboration. Although a limited number of distributed abductive systems
have been developed, none of them is generic enough to support the above requirements.
In this thesis we develop, in the spirit of Logic Programming, a generic and extensible distributed
abductive system that has the potential to target a wide range of distributed problem
solving applications. The underlying distributed inference algorithm incorporates constraint
satisfaction and allows non-ground conditional answers to be computed. Its soundness and
completeness have been proved. The algorithm is customisable in that different inference and
coordination strategies (such as goal selection and agent selection strategies) can be adopted
while maintaining correctness. A customisation that supports confidentiality during problem
solving has been developed, and is used in application domains such as distributed security
policy analysis. Finally, for evaluation purposes, a
flexible experimental environment has been
built for automatically generating different classes of distributed abductive constraint logic programs.
This environment has been used to conduct empirical investigation of the performance
of the customised system
Technology assessment of advanced automation for space missions
Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology
Introduction to the 26th International Conference on Logic Programming Special Issue
This is the preface to the 26th International Conference on Logic Programming
Special IssueComment: 6 page
Multi-agent Confidential Abductive Reasoning
In the context of multi-agent hypothetical reasoning, agents typically have partial knowledge about their environments, and the union of such knowledge is still incomplete to represent the whole world. Thus, given a global query they collaborate with each other to make correct inferences and hypothesis, whilst maintaining global constraints. Most collaborative reasoning systems operate on the assumption that agents can share or communicate any information they have. However, in application domains like multi-agent systems for healthcare or distributed software agents for security policies in coalition networks, confidentiality of knowledge is an additional
primary concern. These agents are required to collaborately compute consistent answers for a query whilst preserving their own private information. This paper addresses this issue showing how this dichotomy between "open communication" in collaborative reasoning and protection of confidentiality can be accommodated. We present a general-purpose distributed abductive logic programming system for multi-agent hypothetical reasoning with confidentiality. Specifically, the system computes consistent conditional answers for a query over a set of distributed normal logic programs with possibly unbound domains and arithmetic constraints, preserving the private information within the logic programs. A case study on security policy analysis in distributed coalition networks is described, as an example of many applications of this system
Pragmatism and the pragmatic turn in cognitive science
This chapter examines the pragmatist approach to cognition and experience and provides some of the conceptual background to the “pragmatic turn” currently underway in cognitive science. Classical pragmatists wrote extensively on cognition from a naturalistic perspective, and many of their views are compatible with contemporary pragmatist approaches such as enactivist, extended, and embodied-Bayesian approaches to cognition. Three principles of a pragmatic approach to cognition frame the discussion: First, thinking is structured by the interaction of an organism with its environment. Second, cognition develops via exploratory inference, which remains a core cognitive ability throughout the life cycle. Finally, inquiry/problem solving begins with genuinely irritating doubts that arise in a situation and is carried out by exploratory inference
Programming in logic without logic programming
In previous work, we proposed a logic-based framework in which computation is
the execution of actions in an attempt to make reactive rules of the form if
antecedent then consequent true in a canonical model of a logic program
determined by an initial state, sequence of events, and the resulting sequence
of subsequent states. In this model-theoretic semantics, reactive rules are the
driving force, and logic programs play only a supporting role.
In the canonical model, states, actions and other events are represented with
timestamps. But in the operational semantics, for the sake of efficiency,
timestamps are omitted and only the current state is maintained. State
transitions are performed reactively by executing actions to make the
consequents of rules true whenever the antecedents become true. This
operational semantics is sound, but incomplete. It cannot make reactive rules
true by preventing their antecedents from becoming true, or by proactively
making their consequents true before their antecedents become true.
In this paper, we characterize the notion of reactive model, and prove that
the operational semantics can generate all and only such models. In order to
focus on the main issues, we omit the logic programming component of the
framework.Comment: Under consideration in Theory and Practice of Logic Programming
(TPLP
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Neurons and symbols: a manifesto
We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of
neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty
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