62 research outputs found
Complexity of Nested Circumscription and Nested Abnormality Theories
The need for a circumscriptive formalism that allows for simple yet elegant
modular problem representation has led Lifschitz (AIJ, 1995) to introduce
nested abnormality theories (NATs) as a tool for modular knowledge
representation, tailored for applying circumscription to minimize exceptional
circumstances. Abstracting from this particular objective, we propose L_{CIRC},
which is an extension of generic propositional circumscription by allowing
propositional combinations and nesting of circumscriptive theories. As shown,
NATs are naturally embedded into this language, and are in fact of equal
expressive capability. We then analyze the complexity of L_{CIRC} and NATs, and
in particular the effect of nesting. The latter is found to be a source of
complexity, which climbs the Polynomial Hierarchy as the nesting depth
increases and reaches PSPACE-completeness in the general case. We also identify
meaningful syntactic fragments of NATs which have lower complexity. In
particular, we show that the generalization of Horn circumscription in the NAT
framework remains CONP-complete, and that Horn NATs without fixed letters can
be efficiently transformed into an equivalent Horn CNF, which implies
polynomial solvability of principal reasoning tasks. Finally, we also study
extensions of NATs and briefly address the complexity in the first-order case.
Our results give insight into the ``cost'' of using L_{CIRC} (resp. NATs) as a
host language for expressing other formalisms such as action theories,
narratives, or spatial theories.Comment: A preliminary abstract of this paper appeared in Proc. Seventeenth
International Joint Conference on Artificial Intelligence (IJCAI-01), pages
169--174. Morgan Kaufmann, 200
The Role of preferences in logic programming: nonmonotonic reasoning, user preferences, decision under uncertainty
Intelligent systems that assist users in fulfilling complex tasks need a concise and processable representation of incomplete and
uncertain information. In order to be able to choose among different options, these systems also need a compact and processable
representation of the concept of preference.
Preferences can provide an effective way to choose the best solutions to a given problem. These solutions can represent the most
plausible states of the world when we model incomplete information, the most satisfactory states of the world when we express
user preferences, or optimal decisions when we make decisions under uncertainty.
Several domains, such as, reasoning under incomplete and uncertain information, user preference modeling, and qualitative
decision making under uncertainty, have benefited from advances on preference representation. In the literature, several symbolic
approaches of nonclassical reasoning have been proposed. Among them, logic programming under answer set semantics offers a
good compromise between symbolic representation and computation of knowledge and several extensions for handling
preferences.
Nevertheless, there are still some open issues to be considered in logic programming. In nonmonotonic reasoning, first, most
approaches assume that exceptions to logic program rules are already specified. However, sometimes, it is possible to consider
implicit preferences based on the specificity of the rules to handle incomplete information. Secondly, the joint handling of
exceptions and uncertainty has received little attention: when information is uncertain, the selection of default rules can be a matter
of explicit preferences and uncertainty. In user preference modeling, although existing logic programming specifications allow to
express user preferences which depend both on incomplete and contextual information, in some applications, some preferences in
some context may be more important than others. Furthermore, more complex preference expressions need to be supported. In
qualitative decision making under uncertainty, existing logic programming-based methodologies for making decisions seem to lack
a satisfactory handling of preferences and uncertainty.
The aim of this dissertation is twofold: 1) to tackle the role played by preferences in logic programming from different perspectives,
and 2) to contribute to this novel field by proposing several frameworks and methods able to address the above issues. To this
end, we will first show how preferences can be used to select default rules in logic programs in an implicit and explicit way. In
particular, we propose (i) a method for selecting logic program rules based on specificity, and (ii) a framework for selecting
uncertain default rules based on explicit preferences and the certainty of the rules. Then, we will see how user preferences can be
modeled and processed in terms of a logic program (iii) in order to manage user profiles in a context-aware system and (iv) in order
to propose a framework for the specification of nested (non-flat) preference expressions. Finally, in the attempt to bridge the gap
between logic programming and qualitative decision under uncertainty, (v) we propose a classical- and a possibilistic-based logic
programming methodology to compute an optimal decision when uncertainty and preferences are matters of degrees.Els sistemes intel.ligents que assisteixen a usuaris en la realització de tasques complexes necessiten
una representació concisa i formal de la informació que permeti un raonament nomonòton
en condicions d’incertesa. Per a poder escollir entre les diferents opcions, aquests
sistemes solen necessitar una representació del concepte de preferència.
Les preferències poden proporcionar una manera efectiva de triar entre les millors solucions
a un problema. Aquestes solucions poden representar els estats del món més plausibles
quan es tracta de modelar informació incompleta, els estats del món més satisfactori
quan expressem preferències de l’usuari, o decisions òptimes quan estem parlant de presa
de decisió incorporant incertesa.
L’ús de les preferències ha beneficiat diferents dominis, com, el raonament en presència
d’informació incompleta i incerta, el modelat de preferències d’usuari, i la presa de decisió
sota incertesa. En la literatura, s’hi troben diferents aproximacions al raonament no clà ssic
basades en una representació simbòlica de la informació. Entre elles, l’enfocament de programació
lògica, utilitzant la semà ntica de answer set, ofereix una bona aproximació entre
representació i processament simbòlic del coneixement, i diferents extensions per gestionar
les preferències.
No obstant això, en programació lògica es poden identificar diferents problemes pel
que fa a la gestió de les preferències. Per exemple, en la majoria d’enfocaments de raonament
no-monòton s’assumeix que les excepcions a default rules d’un programa lògic ja
estan expressades. Però de vegades es poden considerar preferències implÃcites basades en
l’especificitat de les regles per gestionar la informació incompleta. A més, quan la informació
és també incerta, la selecció de default rules pot dependre de preferències explÃcites i de la
incertesa. En el modelatge de preferències del usuari, encara que els formalismes existents
basats en programació lògica permetin expressar preferències que depenen d’informació
contextual i incompleta, en algunes aplicacions, donat un context, algunes preferències
poden ser més importants que unes altres. Per tant, resulta d’interès un llenguatge que
permeti capturar preferències més complexes. En la presa de decisions sota incertesa, les
metodologies basades en programació lògica creades fins ara no ofereixen una solució del
tot satisfactòria pel que fa a la gestió de les preferències i la incertesa.
L’objectiu d’aquesta tesi és doble: 1) estudiar el paper de les preferències en la programació
lògica des de diferents perspectives, i 2) contribuir a aquesta jove à rea d’investigació
proposant diferents marcs teòrics i mètodes per abordar els problemes anteriorment citats.
Per a aquest propòsit veurem com les preferències es poden utilitzar de manera implÃcita i
explÃcita per a la selecció de default rules proposant: (i) un mètode basat en l’especificitat
de les regles, que permeti seleccionar regles en un programa lògic; (ii) un marc teòric per a
la selecció de default rules incertes basat en preferències explÃcites i la incertesa de les regles.
També veurem com les preferències de l’usuari poden ser modelades i processades usant
un enfocament de programació lògica (iii) que suporti la creació d’un mecanisme de gestió
dels perfils dels usuaris en un sistema amb reconeixement del context; (iv) que permeti
proposar un marc teòric capaç d’expressar preferències amb fòrmules imbricades. Per últim,
amb l’objectiu de disminuir la distà ncia entre programació lògica i la presa de decisió
amb incertesa proposem (v) una metodologia basada en programació lògica clà ssica i en
una extensió de la programació lògica que incorpora lògica possibilÃstica per modelar un
problema de presa de decisions i per inferir una decisió òptima.Los sistemas inteligentes que asisten a usuarios en tareas complejas necesitan una representación
concisa y procesable de la información que permita un razonamiento nomonótono
e incierto. Para poder escoger entre las diferentes opciones, estos sistemas suelen
necesitar una representación del concepto de preferencia.
Las preferencias pueden proporcionar una manera efectiva para elegir entre las mejores
soluciones a un problema. Dichas soluciones pueden representar los estados del mundo
más plausibles cuando hablamos de representación de información incompleta, los estados
del mundo más satisfactorios cuando hablamos de preferencias del usuario, o decisiones
óptimas cuando estamos hablando de toma de decisión con incertidumbre.
El uso de las preferencias ha beneficiado diferentes dominios, como, razonamiento en
presencia de información incompleta e incierta, modelado de preferencias de usuario, y
toma de decisión con incertidumbre. En la literatura, distintos enfoques simbólicos de razonamiento
no clásico han sido creados. Entre ellos, la programación lógica con la semántica
de answer set ofrece un buen acercamiento entre representación y procesamiento simbólico
del conocimiento, y diferentes extensiones para manejar las preferencias.
Sin embargo, en programación lógica se pueden identificar diferentes problemas con
respecto al manejo de las preferencias. Por ejemplo, en la mayorÃa de enfoques de razonamiento
no-monótono se asume que las excepciones a default rules de un programa lógico
ya están expresadas. Pero, a veces se pueden considerar preferencias implÃcitas basadas en
la especificidad de las reglas para manejar la información incompleta. Además, cuando la
información es también incierta, la selección de default rules pueden depender de preferencias
explÃcitas y de la incertidumbre. En el modelado de preferencias, aunque los formalismos
existentes basados en programación lógica permitan expresar preferencias que
dependen de información contextual e incompleta, in algunas aplicaciones, algunas preferencias
en un contexto puede ser más importantes que otras. Por lo tanto, un lenguaje
que permita capturar preferencias más complejas es deseable. En la toma de decisiones con
incertidumbre, las metodologÃas basadas en programación lógica creadas hasta ahora no
ofrecen una solución del todo satisfactoria al manejo de las preferencias y la incertidumbre.
El objectivo de esta tesis es doble: 1) estudiar el rol de las preferencias en programación
lógica desde diferentes perspectivas, y 2) contribuir a esta joven área de investigación proponiendo
diferentes marcos teóricos y métodos para abordar los problemas anteriormente
citados. Para este propósito veremos como las preferencias pueden ser usadas de manera implÃcita y explÃcita para la selección de default rules proponiendo: (i) un método para
seleccionar reglas en un programa basado en la especificad de las reglas; (ii) un marco
teórico para la selección de default rules basado en preferencias explÃcitas y incertidumbre.
También veremos como las preferencias del usuario pueden ser modeladas y procesadas
usando un enfoque de programación lógica (iii) para crear un mecanismo de manejo de
los perfiles de los usuarios en un sistema con reconocimiento del contexto; (iv) para crear
un marco teórico capaz de expresar preferencias con formulas anidadas. Por último, con
el objetivo de disminuir la distancia entre programación lógica y la toma de decisión con
incertidumbre proponemos (v) una metodologÃa para modelar un problema de toma de
decisiones y para inferir una decisión óptima usando un enfoque de programación lógica
clásica y uno de programación lógica extendida con lógica posibilÃstica.Sistemi intelligenti, destinati a fornire supporto agli utenti in processi decisionali complessi,
richiedono una rappresentazione dell’informazione concisa, formale e che permetta
di ragionare in maniera non monotona e incerta. Per poter scegliere tra le diverse opzioni,
tali sistemi hanno bisogno di disporre di una rappresentazione del concetto di preferenza
altrettanto concisa e formale.
Le preferenze offrono una maniera efficace per scegliere le miglior soluzioni di un problema.
Tali soluzioni possono rappresentare gli stati del mondo più credibili quando si tratta
di ragionamento non monotono, gli stati del mondo più soddisfacenti quando si tratta delle
preferenze degli utenti, o le decisioni migliori quando prendiamo una decisione in condizioni
di incertezza.
Diversi domini come ad esempio il ragionamento non monotono e incerto, la strutturazione
del profilo utente, e i modelli di decisione in condizioni d’incertezza hanno tratto
beneficio dalla rappresentazione delle preferenze. Nella bibliografia disponibile si possono
incontrare diversi approcci simbolici al ragionamento non classico. Tra questi, la programmazione
logica con answer set semantics offre un buon compromesso tra rappresentazione
simbolica e processamento dell’informazione, e diversi estensioni per la gestione delle preferenze
sono state proposti in tal senso.
Nonostante ció, nella programmazione logica esistono ancora delle problematiche aperte.
Prima di tutto, nella maggior parte degli approcci al ragionamento non monotono, si suppone
che nel programma le eccezioni alle regole siano già specificate. Tuttavia, a volte per
trattare l’informazione incompleta è possibile prendere in considerazione preferenze implicite
basate sulla specificità delle regole. In secondo luogo, la gestione congiunta di eccezioni
e incertezza ha avuto scarsa attenzione: quando l’informazione è incerta, la scelta
di default rule può essere una questione di preferenze esplicite e d’incertezza allo stesso
tempo. Nella creazione di preferenze dell’utente, anche se le specifiche di programmazione
logica esistenti permettono di esprimere preferenze che dipendono sia da un’informazione
incompleta che da una contestuale, in alcune applicazioni talune preferenze possono essere
più importanti di altre, o espressioni più complesse devono essere supportate. In un processo
decisionale con incertezza, le metodologie basate sulla programmazione logica viste
sinora, non offrono una gestione soddisfacente delle preferenze e dell’incertezza.
Lo scopo di questa dissertazione è doppio: 1) chiarire il ruolo che le preferenze giocano
nella programmazione logica da diverse prospettive e 2) contribuire proponendo in questo nuovo settore di ricerca, diversi framework e metodi in grado di affrontare le citate
problematiche. Per prima cosa, dimostreremo come le preferenze possono essere usate per
selezionare default rule in un programma in maniera implicita ed esplicita. In particolare
proporremo: (i) un metodo per la selezione delle regole di un programma logico basato
sulla specificità dell’informazione; (ii) un framework per la selezione di default rule basato
sulle preferenze esplicite e sull’incertezza associata alle regole del programma. Poi, vedremo
come le preferenze degli utenti possono essere modellate attraverso un programma
logico, (iii) per creare il profilo dell’utente in un sistema context-aware, e (iv) per proporre
un framework che supporti la definizione di preferenze complesse. Infine, per colmare le
lacune in programmazione logica applicata a un processo di decisione con incertezza (v)
proporremo una metodologia basata sulla programmazione logica classica e una metodologia
basata su un’estensione della programmazione logica con logica possibilistica
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Using Extended Logic Programs to Formalize Commonsense Reasoning
In this dissertation, we investigate how commonsense reasoning can be formalized by using extended logic programs. In this investigation, we first use extended logic programs to formalize inheritance hierarchies with exceptions by adopting McCarthy's simple abnormality formalism to express uncertain knowledge. In our representation, not only credulous reasoning can be performed but also the ambiguity-blocking inheritance and the ambiguity-propagating inheritance in skeptical reasoning are simulated. In response to the anomalous extension problem, we explore and discover that the intuition underlying commonsense reasoning is a kind of forward reasoning. The unidirectional nature of this reasoning is applied by many reformulations of the Yale shooting problem to exclude the undesired conclusion. We then identify defeasible conclusions in our representation based on the syntax of extended logic programs. A similar idea is also applied to other formalizations of commonsense reasoning to achieve such a purpose
Time-Situated Metacognitive Agency and Other Aspects of Commonsense Reasoning
Much research in commonsense reasoning (CSR) involves use of external representations of an agent's reasoning, based on compelling features of classical logic. However, these advantages come with severe costs, including: omniscience, consistency, static semantics, frozen deadlines, lack of self-knowledge, and lack of expressive power to represent the reasoning of others. Active logic was developed to address many of these, but work to date still leaves serious gaps. The present work focuses on major extensions of active logic to deal with self-knowledge, and their implementation into a newly-developed automated reasoner for commonsense active logic. Dealing with self-knowledge has been designed and implemented in the reasoner via a new treatment of quotation as a form of nesting. More sophisticated varieties of nesting, particularly quasi-quotation mechanisms, have also been developed to extend the basic form of quotation. Active logic and the reasoner are applied to classical issues in CSR, including a treatment of one agent having the knowledge and inferential mechanisms to reason about another's time-situated reasoning
Postdictive Reasoning in Epistemic Action Theory
If an agent executes an action, this will not only change the world physically, but also the agent's knowledge about the world. Therefore the occurrence of an action can be modeled as an epistemic state transition which maps the knowledge state of an agent to a successor knowledge state. For example, consider that an agent in a state s_0 executes an action a. This causes a transition to a state s_1. Subsequently, the agent executes a sensing action a_s, which produces knowledge and causes a transition to a state s_2. With the information which is gained by the sensation, the agent can not only extend its knowledge about s_2, but also infer additional knowledge about the initial state s_0. That is, the agent uses knowledge about the present to retrospectively acquire additional information about the past. We refer to this temporal form of epistemic inference as postdiction. Existing action theories are not capable of efficiently performing postdictive reasoning because they require an exponential number of state variables to represent an agent's knowledge state. The contribution of this thesis is an approximate epistemic action theory which is capable of postdictive reasoning while it requires only a linear number of state variables to represent an agent's knowledge state. In addition, the theory is able to perform a more general temporal form of postdiction, which most existing approaches do not support. We call the theory the h-approximation (HPX) because it explicitly represents historical knowledge about past world states. In addition to the operational semantics of HPX, we present its formalization in terms of Answer Set Programming (ASP) and provide respective soundness results. The ASP implementation allows us to apply HPX in real robotic applications by using off-the-shelf ASP solvers. Specifically, we integrate of HPX in an online planning framework for Cognitive Robotics where planning, plan execution and abductive explanation tasks are interleaved. As a proof-of-concept, we provide a case-study which demonstrates the application of HPX for high-level robot control in a Smart Home. The case-study emphasizes the usefulness of postdiction for abnormality detection in robotics: actions which are performed by robots are often not successful due to unforeseen practical problems. A solution is to verify action success by observing the effects of the action. If the desired effects do not hold after action execution, then one can postdict the existence of an abnormality
EMIL: Extracting Meaning from Inconsistent Language
Developments in formal and computational theories of argumentation reason with inconsistency. Developments in Computational Linguistics extract arguments from large textual corpora. Both developments head in the direction of automated processing and reasoning with inconsistent, linguistic knowledge so as to explain and justify arguments in a humanly accessible form. Yet, there is a gap between the coarse-grained, semi-structured knowledge-bases of computational theories of argumentation and fine-grained, highly-structured inferences from knowledge-bases derived from natural language. We identify several subproblems which must be addressed in order to bridge the gap. We provide a direct semantics for argumentation. It has attractive properties in terms of expressivity and complexity, enables reasoning by cases, and can be more highly structured. For language processing, we work with an existing controlled natural language (CNL), which interfaces with our computational theory of argumentation; the tool processes natural language input, translates them into a form for automated inference engines, outputs argument extensions, then generates natural language statements. The key novel adaptation incorporates the defeasible expression ‘it is usual that’. This is an important, albeit incremental, step to incorporate linguistic expressions of defeasibility. Overall, the novel contribution of the paper is an integrated, end-to-end argumentation system which bridges between automated defeasible reasoning and a natural language interface. Specific novel contributions are the theory of ‘direct semantics’, motivations for our theory, results with respect to the direct semantics, an implementation, experimental results, the tie between the formalisation and the CNL, the introduction into a CNL of a natural language expression of defeasibility, and an ‘engineering’ approach to fine-grained argument analysis
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