153 research outputs found
Current and Future Challenges in Knowledge Representation and Reasoning
Knowledge Representation and Reasoning is a central, longstanding, and active
area of Artificial Intelligence. Over the years it has evolved significantly;
more recently it has been challenged and complemented by research in areas such
as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl
Perspectives workshop was held on Knowledge Representation and Reasoning. The
goal of the workshop was to describe the state of the art in the field,
including its relation with other areas, its shortcomings and strengths,
together with recommendations for future progress. We developed this manifesto
based on the presentations, panels, working groups, and discussions that took
place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge
Representation: its origins, goals, milestones, and current foci; its relation
to other disciplines, especially to Artificial Intelligence; and on its
challenges, along with key priorities for the next decade
Excursions in first-order logic and probability: infinitely many random variables, continuous distributions, recursive programs and beyond
The unification of the first-order logic and probability has been seen as a long-standing concern in philosophy, AI and mathematics. In this talk, I will briefly review our recent results on revisiting that unification. Although there are plenty of approaches in communities such as statistical relational learning, automated planning, and neuro-symbolic AI that leverage and develop languages with logical and probabilistic aspects, they almost always restrict the representation as well as the semantic framework in various ways that does not fully explain how to combine first-order logic and probability theory in a general way. In many cases, this restriction is justified because it may be necessary to focus on practicality and efficiency. However, the search for a restriction-free mathematical theory remains ongoing. In this article, we discuss our recent results regarding the development of languages that support arbitrary quantification, possibly infinitely many ran- dom variables, both discrete and continuous distributions, as well as programming languages built on top of such features to include recursion and branching control
An Answer Set Programming-based Implementation of Epistemic Probabilistic Event Calculus
We describe a general procedure for translating Epistemic Probabilistic Event Calculus (EPEC) action language domains into Answer Set Programs (ASP), and show how the Python-driven features of the ASP solver Clingo can be used to provide efficient computation in this probabilistic setting. EPEC supports probabilistic, epistemic reasoning in domains containing narratives that include both an agent’s own action executions and environmentally triggered events. Some of the agent’s actions may be belief-conditioned, and some may be imperfect sensing actions that alter the strengths of previously held beliefs. We show that our ASP implementation can be used to provide query answers that fully correspond to EPEC’s own declarative, Bayesian-inspired semantics
Logic programming for deliberative robotic task planning
Over the last decade, the use of robots in production and daily life has increased. With increasingly complex tasks and interaction in different environments including humans, robots are required a higher level of autonomy for efficient deliberation. Task planning is a key element of deliberation. It combines elementary operations into a structured plan to satisfy a prescribed goal, given specifications on the robot and the environment. In this manuscript, we present a survey on recent advances in the application of logic programming to the problem of task planning. Logic programming offers several advantages compared to other approaches, including greater expressivity and interpretability which may aid in the development of safe and reliable robots. We analyze different planners and their suitability for specific robotic applications, based on expressivity in domain representation, computational efficiency and software implementation. In this way, we support the robotic designer in choosing the best tool for his application
Scalable Automatic Service Composition using Genetic Algorithms
A composition of simple web services, each dedicated to performing a specific sub- task involved, proves to be a more competitive solution than an equivalent atomic web service for a complex requirement comprised of several sub-tasks. Composite services have been extensively researched and perfected in many aspects for over two decades, owing to benefits such as component re-usability, broader options for composition requesters, and the liberty to specialize for component providers. However, most studies in this field must acknowledge that each web service has a limited context in which it can successfully perform its tasks, the boundaries defined by the internal constraints imposed on the service by its providers. The restricted context-spaces of all such component services define the contextual boundaries of the composite service as a whole when used in a composition, making internal constraints an essential factor in composite service functionality. Due to their limited exposure, no systems have yet been proposed on the large-scale solution repository to cater to the specific verification of internal constraints imposed on components of a composite service. In this thesis, we propose a scalable automatic service composition capable of not only automatically constructing context-aware composite web services with internal constraints positioned for optimal resource utilization but also validating the generated compositions on a large-scale solution repository using the General Intensional Programming System (GIPSY) as a time- and cost-efficient simulation/execution environment
Logic + probabilistic programming + causal laws
Probabilistic planning attempts to incorporate stochastic models directly into the planning process, which is the problem of synthesizing a sequence of actions that achieves some objective for a putative agent. Probabilistic programming has rapidly emerged as a key paradigm to integrate probabilistic concepts with programming languages, which allows one to specify complex probabilistic models using programming primitives like recursion and loops. Probabilistic logic programming aims to further ease the specification of structured probability distributions using first-order logical artefacts. In this article, we briefly discuss the modelling of probabilistic planning through the lens of probabilistic (logic) programming. Although many flavours for such an integration are possible, we focus on two representative examples. The first is an extension to the popular probabilistic logic programming language PROBLOG, which permits the decoration of probabilities on Horn clauses—that is, prolog programs. The second is an extension to the popular agent programming language GOLOG, which permits the logical specification of dynamical systems via actions, effects and observations. The probabilistic extensions thereof emphasize different strengths of probabilistic programming that are particularly useful for non-trivial modelling issues raised in probabilistic planning. Among other things, one can instantiate planning problems with growing and shrinking state spaces, discrete and continuous probability distributions, and non-unique prior distributions in a first-order setting
Ontology-Mediated Probabilistic Model Checking: Extended Version
Probabilistic model checking (PMC) is a well-established method for the quantitative analysis of dynamic systems. On the other hand, description logics (DLs) provide a well-suited formalism to describe and reason about static knowledge, used in many areas to specify domain knowledge in an ontology. We investigate how such knowledge can be integrated into the PMC process, introducing ontology-mediated PMC. Specifically, we propose a formalism that links ontologies to dynamic behaviors specified by guarded commands, the de-facto standard input formalism for PMC tools such as Prism. Further, we present and implement a technique for their analysis relying on existing DL-reasoning and PMC tools. This way, we enable the application of standard PMC techniques to analyze knowledge-intensive systems. Our approach is implemented and evaluated on a multi-server system case study, where different DL-ontologies are used to provide specifications of different server platforms and situations the system is executed in
Inference and Learning with Planning Models
[ES] Inferencia y aprendizaje son los actos de razonar sobre evidencia recogida con el fin de alcanzar conclusiones lógicas sobre el proceso que la originó. En el contexto de un modelo de espacio de estados, inferencia y aprendizaje se refieren normalmente a explicar el comportamiento pasado de un agente, predecir sus acciones futuras, o identificar su modelo. En esta tesis, presentamos un marco para inferencia y aprendizaje en el modelo de espacio de estados subyacente al modelo de planificación clásica, y formulamos una paleta de problemas de inferencia y aprendizaje bajo este paraguas unificador. También desarrollamos métodos efectivos basados en planificación que nos permiten resolver estos problemas utilizando algoritmos de planificación genéricos del estado del arte. Mostraremos que un gran número de problemas de inferencia y aprendizaje claves que han sido tratados como desconectados se pueden formular de forma cohesiva y resolver siguiendo procedimientos homogéneos usando nuestro marco. Además, nuestro trabajo abre las puertas a nuevas aplicaciones para tecnologÃa de planificación ya que resalta las caracterÃsticas que hacen que el modelo de espacio de estados de planificación clásica sea diferente a los demás modelos.[CA] Inferència i aprenentatge són els actes de raonar sobre evidència arreplegada a fi d'aconseguir conclusions lògiques sobre el procés que la va originar. En el context d'un model d'espai d'estats, inferència i aprenentatge es referixen normalment a explicar el comportament passat d'un agent, predir les seues accions futures, o identificar el seu model. En esta tesi, presentem un marc per a inferència i aprenentatge en el model d'espai d'estats subjacent al model de planificació clà ssica, i formulem una paleta de problemes d'inferència i aprenentatge davall este paraigua unificador. També desenrotllem mètodes efectius basats en planificació que ens permeten resoldre estos problemes utilitzant algoritmes de planificació genèrics de l'estat de l'art. Mostrarem que un gran nombre de problemes d'inferència i aprenentatge claus que han sigut tractats com desconnectats es poden formular de forma cohesiva i resoldre seguint procediments homogenis usant el nostre marc. A més, el nostre treball obri les portes a noves aplicacions per a tecnologia de planificació ja que ressalta les caracterÃstiques que fan que el model d'espai d'estats de planificació clà ssica siga diferent dels altres models.[EN] Inference and learning are the acts of reasoning about some collected evidence in order to reach a logical conclusion regarding the process that originated it. In the context of a state-space model, inference and learning are usually concerned with explaining an agent's past behaviour, predicting its future actions or identifying its model. In this thesis, we present a framework for inference and learning in the state-space model underlying the classical planning model, and formulate a palette of inference and learning problems under this unifying umbrella. We also develop effective planning-based approaches to solve these problems using off-the-shelf, state-of-the-art planning algorithms. We will show that several core inference and learning problems that previous research has treated as disconnected can be formulated in a cohesive way and solved following homogeneous procedures using the proposed framework. Further, our work opens the way for new applications of planning technology as it highlights the features that make the state-space model of classical planning different from other models.The work developed in this doctoral thesis has been possible thanks to the FPU16/03184 fellowship that I have enjoyed for the duration of my PhD studies. I have also been supported by my advisors’ grants TIN2017-88476-C2-1-R, TIN2014-55637-C2-2-R-AR, and RYC-2015-18009.Aineto GarcÃa, D. (2022). Inference and Learning with Planning Models [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/18535
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