8 research outputs found
A belief-desire-intention architechture with a logic-based planner for agents in stochastic domains
This dissertation investigates high-level decision making for agents that are both goal and utility
driven. We develop a partially observable Markov decision process (POMDP) planner which
is an extension of an agent programming language called DTGolog, itself an extension of the
Golog language. Golog is based on a logic for reasoning about actionâthe situation calculus.
A POMDP planner on its own cannot cope well with dynamically changing environments
and complicated goals. This is exactly a strength of the belief-desire-intention (BDI) model:
BDI theory has been developed to design agents that can select goals intelligently, dynamically
abandon and adopt new goals, and yet commit to intentions for achieving goals. The contribution
of this research is twofold: (1) developing a relational POMDP planner for cognitive
robotics, (2) specifying a preliminary BDI architecture that can deal with stochasticity in action
and perception, by employing the planner.ComputingM. Sc. (Computer Science
Agent programming in the cognitive era
It is claimed that, in the nascent âCognitive Eraâ, intelligent systems will be trained using machine learning techniques rather than programmed by software developers. A contrary point of view argues that machine learning has limitations, and, taken in isolation, cannot form the basis of autonomous systems capable of intelligent behaviour in complex environments. In this paper, we explore the contributions that agent-oriented programming can make to the development of future intelligent systems. We briefly review the state of the art in agent programming, focussing particularly on BDI-based agent programming languages, and discuss previous work on integrating AI techniques (including machine learning) in agent-oriented programming. We argue that the unique strengths of BDI agent languages provide an ideal framework for integrating the wide range of AI capabilities necessary for progress towards the next-generation of intelligent systems. We identify a range of possible approaches to integrating AI into a BDI agent architecture. Some of these approaches, e.g., âAI as a serviceâ, exploit immediate synergies between rapidly maturing AI techniques and agent programming, while others, e.g., âAI embedded into agentsâ raise more fundamental research questions, and we sketch a programme of research directed towards identifying the most appropriate ways of integrating AI capabilities into agent programs
Formalisms for agents reasoning with stochastic actions and perceptions.
Ph. D. University of KwaZulu-Natal, Durban 2014.The thesis reports on the development of a sequence of logics (formal languages based on mathematical
logic) to deal with a class of uncertainty that agents may encounter. More accurately, the
logics are meant to be used for allowing robots or software agents to reason about the uncertainty
they have about the effects of their actions and the noisiness of their observations. The approach
is to take the well-established formalism called the partially observable Markov decision process
(POMDP) as an underlying formalism and then design a modal logic based on POMDP theory to
allow an agent to reason with a knowledge-base (including knowledge about the uncertainties).
First, three logics are designed, each one adding one or more important features for reasoning in
the class of domains of interest (i.e., domains where stochastic action and sensing are considered).
The final logic, called the Stochastic Decision Logic (SDL) combines the three logics into a coherent
formalism, adding three important notions for reasoning about stochastic decision-theoretic
domains: (i) representation of and reasoning about degrees of belief in a statement, given stochastic
knowledge, (ii) representation of and reasoning about the expected future rewards of a sequence
of actions and (iii) the progression or update of an agentâs epistemic, stochastic knowledge.
For all the logics developed in this thesis, entailment is defined, that is, whether a sentence logically
follows from a knowledge-base. Decision procedures for determining entailment are developed,
and they are all proved sound, complete and terminating. The decision procedures all
employ tableau calculi to deal with the traditional logical aspects, and systems of equations and
inequalities to deal with the probabilistic aspects.
Besides promoting the compact representation of POMDP models, and the power that logic brings
to the automation of reasoning, the Stochastic Decision Logic is novel and significant in that it
allows the agent to determine whether or not a set of sentences is entailed by an arbitrarily precise
specification of a POMDP model, where this is not possible with standard POMDPs.
The research conducted for this thesis has resulted in several publications and has been presented
at several workshops, symposia and conferences
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 agentsâ
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 SALMAâs 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 fuÌr die Simulation und die statistische ModellpruÌfung (Model Checking)
von Multiagentensystemen.
Der Begriff âStatistisches Model Checkingâ beschreibt modellbasierte approximative
Verifikationsmethoden, die insbesondere dazu eingesetzt werden
können, um den unvermeidlichen Skalierbarkeitsproblemen von exakten Methoden
zu entgehen. Im Gegensatz zu bisherigen AnsÀtzen werden in SALMA die
Mechanismen des simulierten Systems mithilfe logischer Axiome beschrieben,
die auf dem etablierten SituationskalkuÌl aufbauen. Die dadurch entstehende
prÀdikatenlogische Struktur des Systemmodells wird ausgenutzt um ein Model
Checking Modul zu integrieren, das seinerseits eine prÀdikatenlogische Variante
der linearen temporalen Logik (LTL) verwendet. In Kombination mit
einer prozeduralen und prozessorientierten Sprache fuÌr die Beschreibung von
Agentenverhalten entsteht eine ausdrucksstarke und flexible Plattform fuÌr 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 SituationskalkuÌl 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) unterstuÌtzt,
in denen Agenten mit ihrer physischen Umgebung interagieren. Unter
anderem wird eine generische, auf dem SituationskalkuÌl basierende, Axiomatisierung
von Prozessen beschrieben, in denen Informationen innerhalb von
Multiagentensystemen transferiert werden â beispielsweise in Form von Sensor-
Messwerten oder Netzwerkpaketen. Dabei werden ausdruÌcklich die unvermeidbaren
stochastischen Effekte und Echtzeitanforderungen in cyber-physischen
Systemen beruÌcksichtigt.
Um statistisches Model Checking mit SALMA auch fuÌr komplexere Problemstellungen
zu ermöglichen, wurde ein Mechanismus fuÌr die effiziente Auswertung
von prÀdikatenlogischen LTL-Formeln entwickelt. Insbesondere beinhaltet
der vorgestellte Algorithmus eine Intervall-basierte ReprÀsentation des
Auswertungszustands, sowie einige andere OptimierungsansÀtze zur Vermeidung
von unnötigen Berechnungsschritten.
Insgesamt war es das Ziel dieser Dissertation, eine Lösung fuÌr 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
genuÌgt. TatsĂ€chlich legen erste Ergebnisse und Erfahrungen aus
mehreren kleinen bis mittelgroĂen Experimenten nahe, dass SALMA diesen
Zielen gerecht wird
Pseudo-contractions as Gentle Repairs
Updating a knowledge base to remove an unwanted consequence is a challenging task. Some of the original sentences must be either deleted or weakened in such a way that the sentence to be removed is no longer entailed by the resulting set. On the other hand, it is desirable that the existing knowledge be preserved as much as possible, minimising the loss of information. Several approaches to this problem can be found in the literature. In particular, when the knowledge is represented by an ontology, two different families of frameworks have been developed in the literature in the past decades with numerous ideas in common but with little interaction between the communities: applications of AGM-like Belief Change and justification-based Ontology Repair. In this paper, we investigate the relationship between pseudo-contraction operations and gentle repairs. Both aim to avoid the complete deletion of sentences when replacing them with weaker versions is enough to prevent the entailment of the unwanted formula. We show the correspondence between concepts on both sides and investigate under which conditions they are equivalent. Furthermore, we propose a unified notation for the two approaches, which might contribute to the integration of the two areas
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 agentsâ
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 SALMAâs 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 fuÌr die Simulation und die statistische ModellpruÌfung (Model Checking)
von Multiagentensystemen.
Der Begriff âStatistisches Model Checkingâ beschreibt modellbasierte approximative
Verifikationsmethoden, die insbesondere dazu eingesetzt werden
können, um den unvermeidlichen Skalierbarkeitsproblemen von exakten Methoden
zu entgehen. Im Gegensatz zu bisherigen AnsÀtzen werden in SALMA die
Mechanismen des simulierten Systems mithilfe logischer Axiome beschrieben,
die auf dem etablierten SituationskalkuÌl aufbauen. Die dadurch entstehende
prÀdikatenlogische Struktur des Systemmodells wird ausgenutzt um ein Model
Checking Modul zu integrieren, das seinerseits eine prÀdikatenlogische Variante
der linearen temporalen Logik (LTL) verwendet. In Kombination mit
einer prozeduralen und prozessorientierten Sprache fuÌr die Beschreibung von
Agentenverhalten entsteht eine ausdrucksstarke und flexible Plattform fuÌr 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 SituationskalkuÌl 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) unterstuÌtzt,
in denen Agenten mit ihrer physischen Umgebung interagieren. Unter
anderem wird eine generische, auf dem SituationskalkuÌl basierende, Axiomatisierung
von Prozessen beschrieben, in denen Informationen innerhalb von
Multiagentensystemen transferiert werden â beispielsweise in Form von Sensor-
Messwerten oder Netzwerkpaketen. Dabei werden ausdruÌcklich die unvermeidbaren
stochastischen Effekte und Echtzeitanforderungen in cyber-physischen
Systemen beruÌcksichtigt.
Um statistisches Model Checking mit SALMA auch fuÌr komplexere Problemstellungen
zu ermöglichen, wurde ein Mechanismus fuÌr die effiziente Auswertung
von prÀdikatenlogischen LTL-Formeln entwickelt. Insbesondere beinhaltet
der vorgestellte Algorithmus eine Intervall-basierte ReprÀsentation des
Auswertungszustands, sowie einige andere OptimierungsansÀtze zur Vermeidung
von unnötigen Berechnungsschritten.
Insgesamt war es das Ziel dieser Dissertation, eine Lösung fuÌr 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
genuÌgt. TatsĂ€chlich legen erste Ergebnisse und Erfahrungen aus
mehreren kleinen bis mittelgroĂen Experimenten nahe, dass SALMA diesen
Zielen gerecht wird
Extending DTGolog with options
Recently Boutilier et al. (2000) proposed the language DT-GOLOG which combines explicit agent programming with decision theory. The motivation is that a user often has some idea about how to go about solving a particular problem yet a