216,467 research outputs found
Analysing the behaviour of robot teams through relational sequential pattern mining
This report outlines the use of a relational representation in a Multi-Agent
domain to model the behaviour of the whole system. A desired property in this
systems is the ability of the team members to work together to achieve a common
goal in a cooperative manner. The aim is to define a systematic method to
verify the effective collaboration among the members of a team and comparing
the different multi-agent behaviours. Using external observations of a
Multi-Agent System to analyse, model, recognize agent behaviour could be very
useful to direct team actions. In particular, this report focuses on the
challenge of autonomous unsupervised sequential learning of the team's
behaviour from observations. Our approach allows to learn a symbolic sequence
(a relational representation) to translate raw multi-agent, multi-variate
observations of a dynamic, complex environment, into a set of sequential
behaviours that are characteristic of the team in question, represented by a
set of sequences expressed in first-order logic atoms. We propose to use a
relational learning algorithm to mine meaningful frequent patterns among the
relational sequences to characterise team behaviours. We compared the
performance of two teams in the RoboCup four-legged league environment, that
have a very different approach to the game. One uses a Case Based Reasoning
approach, the other uses a pure reactive behaviour.Comment: 25 page
Multi-Agent Only-Knowing Revisited
Levesque introduced the notion of only-knowing to precisely capture the
beliefs of a knowledge base. He also showed how only-knowing can be used to
formalize non-monotonic behavior within a monotonic logic. Despite its appeal,
all attempts to extend only-knowing to the many agent case have undesirable
properties. A belief model by Halpern and Lakemeyer, for instance, appeals to
proof-theoretic constructs in the semantics and needs to axiomatize validity as
part of the logic. It is also not clear how to generalize their ideas to a
first-order case. In this paper, we propose a new account of multi-agent
only-knowing which, for the first time, has a natural possible-world semantics
for a quantified language with equality. We then provide, for the propositional
fragment, a sound and complete axiomatization that faithfully lifts Levesque's
proof theory to the many agent case. We also discuss comparisons to the earlier
approach by Halpern and Lakemeyer.Comment: Appears in Principles of Knowledge Representation and Reasoning 201
Generating Functions For Kernels of Digraphs (Enumeration & Asymptotics for Nim Games)
In this article, we study directed graphs (digraphs) with a coloring
constraint due to Von Neumann and related to Nim-type games. This is equivalent
to the notion of kernels of digraphs, which appears in numerous fields of
research such as game theory, complexity theory, artificial intelligence
(default logic, argumentation in multi-agent systems), 0-1 laws in monadic
second order logic, combinatorics (perfect graphs)... Kernels of digraphs lead
to numerous difficult questions (in the sense of NP-completeness,
#P-completeness). However, we show here that it is possible to use a generating
function approach to get new informations: we use technique of symbolic and
analytic combinatorics (generating functions and their singularities) in order
to get exact and asymptotic results, e.g. for the existence of a kernel in a
circuit or in a unicircuit digraph. This is a first step toward a
generatingfunctionology treatment of kernels, while using, e.g., an approach "a
la Wright". Our method could be applied to more general "local coloring
constraints" in decomposable combinatorial structures.Comment: Presented (as a poster) to the conference Formal Power Series and
Algebraic Combinatorics (Vancouver, 2004), electronic proceeding
A Dynamic Epistemic Logic for Abstract Argumentation
This paper introduces a multi-agent dynamic epistemic logic for abstract argumenta-
tion. Its main motivation is to build a general framework for modelling the dynamics
of a debate, which entails reasoning about goals, beliefs, as well as policies of com-
munication and information update by the participants. After locating our proposal
and introducing the relevant tools from abstract argumentation, we proceed to build a
three-tiered logical approach. At the first level, we use the language of propositional
logic to encode states of a multi-agent debate. This language allows to specify which
arguments any agent is aware of, as well as their subjective justification status. We
then extend our language and semantics to that of epistemic logic, in order to model
individuals’ beliefs about the state of the debate, which includes uncertainty about the
information available to others. As a third step, we introduce a framework of dynamic
epistemic logic and its semantics, which is essentially based on so-called event models
with factual change. We provide completeness results for a number of systems and
show how existing formalisms for argumentation dynamics and unquantified uncerSynthese
tainty can be reduced to their semantics. The resulting framework allows reasoning
about subtle epistemic and argumentative updates—such as the effects of different
levels of trust in a source—and more in general about the epistemic dimensions of
strategic communication
Simulation and statistical model-checking of logic-based multi-agent system models
This thesis presents SALMA (Simulation and Analysis of Logic-Based Multi-
Agent Models), a new approach for simulation and statistical model checking
of multi-agent system models.
Statistical model checking is a relatively new branch of model-based approximative
verification methods that help to overcome the well-known scalability
problems of exact model checking. In contrast to existing solutions,
SALMA specifies the mechanisms of the simulated system by means of logical
axioms based upon the well-established situation calculus. Leveraging
the resulting first-order logic structure of the system model, the simulation
is coupled with a statistical model-checker that uses a first-order variant of
time-bounded linear temporal logic (LTL) for describing properties. This is
combined with a procedural and process-based language for describing agent
behavior. Together, these parts create a very expressive framework for modeling
and verification that allows direct fine-grained reasoning about the agents’
interaction with each other and with their (physical) environment.
SALMA extends the classical situation calculus and linear temporal logic
(LTL) with means to address the specific requirements of multi-agent simulation
models. In particular, cyber-physical domains are considered where
the agents interact with their physical environment. Among other things,
the thesis describes a generic situation calculus axiomatization that encompasses
sensing and information transfer in multi agent systems, for instance
sensor measurements or inter-agent messages. The proposed model explicitly
accounts for real-time constraints and stochastic effects that are inevitable in
cyber-physical systems.
In order to make SALMA’s statistical model checking facilities usable also
for more complex problems, a mechanism for the efficient on-the-fly evaluation
of first-order LTL properties was developed. In particular, the presented algorithm
uses an interval-based representation of the formula evaluation state
together with several other optimization techniques to avoid unnecessary computation.
Altogether, the goal of this thesis was to create an approach for simulation
and statistical model checking of multi-agent systems that builds upon
well-proven logical and statistical foundations, but at the same time takes a
pragmatic software engineering perspective that considers factors like usability,
scalability, and extensibility. In fact, experience gained during several small
to mid-sized experiments that are presented in this thesis suggest that the
SALMA approach seems to be able to live up to these expectations.In dieser Dissertation wird SALMA (Simulation and Analysis of Logic-Based
Multi-Agent Models) vorgestellt, ein im Rahmen dieser Arbeit entwickelter
Ansatz für die Simulation und die statistische Modellprüfung (Model Checking)
von Multiagentensystemen.
Der Begriff „Statistisches Model Checking” beschreibt modellbasierte approximative
Verifikationsmethoden, die insbesondere dazu eingesetzt werden
können, um den unvermeidlichen Skalierbarkeitsproblemen von exakten Methoden
zu entgehen. Im Gegensatz zu bisherigen Ansätzen werden in SALMA die
Mechanismen des simulierten Systems mithilfe logischer Axiome beschrieben,
die auf dem etablierten Situationskalkül aufbauen. Die dadurch entstehende
prädikatenlogische Struktur des Systemmodells wird ausgenutzt um ein Model
Checking Modul zu integrieren, das seinerseits eine prädikatenlogische Variante
der linearen temporalen Logik (LTL) verwendet. In Kombination mit
einer prozeduralen und prozessorientierten Sprache für die Beschreibung von
Agentenverhalten entsteht eine ausdrucksstarke und flexible Plattform für die
Modellierung und Verifikation von Multiagentensystemen. Sie ermöglicht eine
direkte und feingranulare Beschreibung der Interaktionen sowohl zwischen
Agenten als auch von Agenten mit ihrer (physischen) Umgebung.
SALMA erweitert den klassischen Situationskalkül und die lineare temporale
Logik (LTL) um Elemente und Konzepte, die auf die spezifischen Anforderungen
bei der Simulation und Modellierung von Multiagentensystemen
ausgelegt sind. Insbesondere werden cyber-physische Systeme (CPS) unterstützt,
in denen Agenten mit ihrer physischen Umgebung interagieren. Unter
anderem wird eine generische, auf dem Situationskalkül basierende, Axiomatisierung
von Prozessen beschrieben, in denen Informationen innerhalb von
Multiagentensystemen transferiert werden – beispielsweise in Form von Sensor-
Messwerten oder Netzwerkpaketen. Dabei werden ausdrücklich die unvermeidbaren
stochastischen Effekte und Echtzeitanforderungen in cyber-physischen
Systemen berücksichtigt.
Um statistisches Model Checking mit SALMA auch für komplexere Problemstellungen
zu ermöglichen, wurde ein Mechanismus für die effiziente Auswertung
von prädikatenlogischen LTL-Formeln entwickelt. Insbesondere beinhaltet
der vorgestellte Algorithmus eine Intervall-basierte Repräsentation des
Auswertungszustands, sowie einige andere Optimierungsansätze zur Vermeidung
von unnötigen Berechnungsschritten.
Insgesamt war es das Ziel dieser Dissertation, eine Lösung für Simulation
und statistisches Model Checking zu schaffen, die einerseits auf fundierten
logischen und statistischen Grundlagen aufbaut, auf der anderen Seite jedoch
auch pragmatischen Gesichtspunkten wie Benutzbarkeit oder Erweiterbarkeit
genügt. Tatsächlich legen erste Ergebnisse und Erfahrungen aus
mehreren kleinen bis mittelgroßen Experimenten nahe, dass SALMA diesen
Zielen gerecht wird
Reputation-based decisions for logic-based cognitive agents
Computational trust and reputation models have been recognized as one of the key technologies required to design and implement agent systems. These models manage and aggregate the information needed by agents to efficiently perform partner selection in uncertain situations. For simple applications, a game theoretical approach similar to that used in most models can suffice. However, if we want to undertake problems found in socially complex virtual societies, we need more sophisticated trust and reputation systems. In this context, reputation-based decisions that agents make take on special relevance and can be as important as the reputation model itself. In this paper, we propose a possible integration of a cognitive reputation model, Repage, into a cognitive BDI agent. First, we specify a belief logic capable to capture the semantics of Repage information, which encodes probabilities. This logic is defined by means of a two first-order languages hierarchy, allowing the specification of axioms as first-order theories. The belief logic integrates the information coming from Repage in terms if image and reputation, and combines them, defining a typology of agents depending of such combination. We use this logic to build a complete graded BDI model specified as a multi-context system where beliefs, desires, intentions and plans interact among each other to perform a BDI reasoning. We conclude the paper with an example and a related work section that compares our approach with current state-of-the-art models. © 2010 The Author(s).This work was supported by the projects AEI (TIN2006-15662-C02-01), AT (CONSOLIDER CSD20070022, INGENIO 2010), LiquidPub (STREP FP7-213360), RepBDI (Intramural 200850I136) and by the Generalitat de Catalunya under the grant 2005-SGR-00093.Peer Reviewe
編集後記、執筆者紹介、奥付
This paper outlines the use of a relational representation in a Multi-Agent domain to model the behaviour of the whole system. The aim of this work is to define a general systematic method to verify the effective collaboration among the members of a team and to compare the different multi-agent behaviours, using external observations of a Multi-Agent System. Observing and analysing the behavior of a such system is a difficult task. Our approach allows to learn sequential behaviours from raw multi-agent observations of a dynamic, complex environment, represented by a set of sequences expressed in first-order logic. In order to discover the underlying knowledge to characterise team behaviours, we propose to use a relational learning algorithm to mine meaningful frequent patterns among the relational sequences. We compared the performance of two soccer teams in a simulated environment, each based on very different behavioural approaches: While one uses a more deliberative strategy, the other one uses a pure reactive one
Simulation and statistical model-checking of logic-based multi-agent system models
This thesis presents SALMA (Simulation and Analysis of Logic-Based Multi-
Agent Models), a new approach for simulation and statistical model checking
of multi-agent system models.
Statistical model checking is a relatively new branch of model-based approximative
verification methods that help to overcome the well-known scalability
problems of exact model checking. In contrast to existing solutions,
SALMA specifies the mechanisms of the simulated system by means of logical
axioms based upon the well-established situation calculus. Leveraging
the resulting first-order logic structure of the system model, the simulation
is coupled with a statistical model-checker that uses a first-order variant of
time-bounded linear temporal logic (LTL) for describing properties. This is
combined with a procedural and process-based language for describing agent
behavior. Together, these parts create a very expressive framework for modeling
and verification that allows direct fine-grained reasoning about the agents’
interaction with each other and with their (physical) environment.
SALMA extends the classical situation calculus and linear temporal logic
(LTL) with means to address the specific requirements of multi-agent simulation
models. In particular, cyber-physical domains are considered where
the agents interact with their physical environment. Among other things,
the thesis describes a generic situation calculus axiomatization that encompasses
sensing and information transfer in multi agent systems, for instance
sensor measurements or inter-agent messages. The proposed model explicitly
accounts for real-time constraints and stochastic effects that are inevitable in
cyber-physical systems.
In order to make SALMA’s statistical model checking facilities usable also
for more complex problems, a mechanism for the efficient on-the-fly evaluation
of first-order LTL properties was developed. In particular, the presented algorithm
uses an interval-based representation of the formula evaluation state
together with several other optimization techniques to avoid unnecessary computation.
Altogether, the goal of this thesis was to create an approach for simulation
and statistical model checking of multi-agent systems that builds upon
well-proven logical and statistical foundations, but at the same time takes a
pragmatic software engineering perspective that considers factors like usability,
scalability, and extensibility. In fact, experience gained during several small
to mid-sized experiments that are presented in this thesis suggest that the
SALMA approach seems to be able to live up to these expectations.In dieser Dissertation wird SALMA (Simulation and Analysis of Logic-Based
Multi-Agent Models) vorgestellt, ein im Rahmen dieser Arbeit entwickelter
Ansatz für die Simulation und die statistische Modellprüfung (Model Checking)
von Multiagentensystemen.
Der Begriff „Statistisches Model Checking” beschreibt modellbasierte approximative
Verifikationsmethoden, die insbesondere dazu eingesetzt werden
können, um den unvermeidlichen Skalierbarkeitsproblemen von exakten Methoden
zu entgehen. Im Gegensatz zu bisherigen Ansätzen werden in SALMA die
Mechanismen des simulierten Systems mithilfe logischer Axiome beschrieben,
die auf dem etablierten Situationskalkül aufbauen. Die dadurch entstehende
prädikatenlogische Struktur des Systemmodells wird ausgenutzt um ein Model
Checking Modul zu integrieren, das seinerseits eine prädikatenlogische Variante
der linearen temporalen Logik (LTL) verwendet. In Kombination mit
einer prozeduralen und prozessorientierten Sprache für die Beschreibung von
Agentenverhalten entsteht eine ausdrucksstarke und flexible Plattform für die
Modellierung und Verifikation von Multiagentensystemen. Sie ermöglicht eine
direkte und feingranulare Beschreibung der Interaktionen sowohl zwischen
Agenten als auch von Agenten mit ihrer (physischen) Umgebung.
SALMA erweitert den klassischen Situationskalkül und die lineare temporale
Logik (LTL) um Elemente und Konzepte, die auf die spezifischen Anforderungen
bei der Simulation und Modellierung von Multiagentensystemen
ausgelegt sind. Insbesondere werden cyber-physische Systeme (CPS) unterstützt,
in denen Agenten mit ihrer physischen Umgebung interagieren. Unter
anderem wird eine generische, auf dem Situationskalkül basierende, Axiomatisierung
von Prozessen beschrieben, in denen Informationen innerhalb von
Multiagentensystemen transferiert werden – beispielsweise in Form von Sensor-
Messwerten oder Netzwerkpaketen. Dabei werden ausdrücklich die unvermeidbaren
stochastischen Effekte und Echtzeitanforderungen in cyber-physischen
Systemen berücksichtigt.
Um statistisches Model Checking mit SALMA auch für komplexere Problemstellungen
zu ermöglichen, wurde ein Mechanismus für die effiziente Auswertung
von prädikatenlogischen LTL-Formeln entwickelt. Insbesondere beinhaltet
der vorgestellte Algorithmus eine Intervall-basierte Repräsentation des
Auswertungszustands, sowie einige andere Optimierungsansätze zur Vermeidung
von unnötigen Berechnungsschritten.
Insgesamt war es das Ziel dieser Dissertation, eine Lösung für Simulation
und statistisches Model Checking zu schaffen, die einerseits auf fundierten
logischen und statistischen Grundlagen aufbaut, auf der anderen Seite jedoch
auch pragmatischen Gesichtspunkten wie Benutzbarkeit oder Erweiterbarkeit
genügt. Tatsächlich legen erste Ergebnisse und Erfahrungen aus
mehreren kleinen bis mittelgroßen Experimenten nahe, dass SALMA diesen
Zielen gerecht wird
Clausal reasoning for branching-time logics
Computation Tree Logic (CTL) is a branching-time temporal logic whose underlying model of time is a choice of possibilities branching into the future. It has been used in a wide variety of areas in Computer Science and Artificial Intelligence, such as temporal databases, hardware verification, program reasoning, multi-agent systems, and concurrent and distributed systems. In this thesis, firstly we present a refined clausal resolution calculus R�,S CTL for CTL. The calculus requires a polynomial time computable transformation of an arbitrary CTL formula to an equisatisfiable clausal normal form formulated in an extension of CTL with indexed existential path quantifiers. The calculus itself consists of eight step resolution rules, two eventuality resolution rules and two rewrite rules, which can be used as the basis for an EXPTIME decision procedure for the satisfiability problem of CTL. We give a formal semantics for the clausal normal form, establish that the clausal normal form transformation preserves satisfiability, provide proofs for the soundness and completeness of the calculus R�,S CTL, and discuss the complexity of the decision procedure based on R�,S CTL. As R�,S CTL is based on the ideas underlying Bolotov’s clausal resolution calculus for CTL, we provide a comparison between our calculus R�,S CTL and Bolotov’s calculus for CTL in order to show that R�,S CTL improves Bolotov’s calculus in many areas. In particular, our calculus is designed to allow first-order resolution techniques to emulate resolution rules of R�,S CTL so that R�,S CTL can be implemented by reusing any first-order resolution theorem prover. Secondly, we introduce CTL-RP, our implementation of the calculus R�,S CTL. CTL-RP is the first implemented resolution-based theorem prover for CTL. The prover takes an arbitrary CTL formula as input and transforms it into a set of CTL formulae in clausal normal form. Furthermore, in order to use first-order techniques, formulae in clausal normal form are transformed into firstorder formulae, except for those formulae related to eventualities, i.e. formulae containing the eventuality operator 3. To implement step resolution and rewrite rules of the calculus R�,S CTL, we present an approach that uses first-order ordered resolution with selection to emulate the step resolution rules and related proofs. This approach enables us to make use of a first-order theorem prover, which implements the first-order ordered resolution with selection, in order to realise our calculus. Following this approach, CTL-RP utilises the first-order theorem prover SPASS to conduct resolution inferences for CTL and is implemented as a modification of SPASS. In particular, to implement the eventuality resolution rules, CTL-RP augments SPASS with an algorithm, called loop search algorithm for tackling eventualities in CTL. To study the performance of CTL-RP, we have compared CTL-RP with a tableau-based theorem prover for CTL. The experiments show good performance of CTL-RP. i ii ABSTRACT Thirdly, we apply the approach we used to develop R�,S CTL to the development of a clausal resolution calculus for a fragment of Alternating-time Temporal Logic (ATL). ATL is a generalisation and extension of branching-time temporal logic, in which the temporal operators are parameterised by sets of agents. Informally speaking, CTL formulae can be treated as ATL formulae with a single agent. Selective quantification over paths enables ATL to explicitly express coalition abilities, which naturally makes ATL a formalism for specification and verification of open systems and game-like multi-agent systems. In this thesis, we focus on the Next-time fragment of ATL (XATL), which is closely related to Coalition Logic. The satisfiability problem of XATL has lower complexity than ATL but there are still many applications in various strategic games and multi-agent systems that can be represented in and reasoned about in XATL. In this thesis, we present a resolution calculus RXATL for XATL to tackle its satisfiability problem. The calculus requires a polynomial time computable transformation of an arbitrary XATL formula to an equi-satisfiable clausal normal form. The calculus itself consists of a set of resolution rules and rewrite rules. We prove the soundness of the calculus and outline a completeness proof for the calculus RXATL. Also, we intend to extend our calculus RXATL to full ATL in the future
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