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Reusable components for knowledge modelling
In this work I illustrate an approach to the development of a library of problem solving components for knowledge modelling. This approach is based on an epistemological modelling framework, the Task/Method/Domain/Application (TMDA) model, and on a principled methodology, which provide an integrated view of both library construction and application development by reuse.
The starting point of the proposed approach is given by a task ontology. This formalizes a conceptual viewpoint over a class of problems, thus providing a task-specific framework, which can be used to drive the construction of a task model through a process of model-based knowledge acquisition. The definitions in the task ontology provide the initial elements of a task-specific library of problem solving components.
In order to move from problem specification to problem solving, a generic, i.e. taskindependent, model of problem solving as search is introduced, and instantiated in terms of the concepts in the relevant task ontology, say T. The result is a task-specific, but method-independent, problem solving model. This generic problem solving model provides the foundation from which alternative problem solving methods for a class of tasks can be defined. Specifically, the generic problem solving model provides i) a highly generic method ontology, say M; ii) a set of generic building blocks (generic tasks), which can be used to construct task-specific problem solving methods; and iii) an initial problem solving method, which can be characterized as the most generic problem solving method, which subscribes to M and is applicable to T. More specific problem solving methods can then be (re-)constructed from the generic problem solving model through a process of method/ontology specialization and method-to-task application.
The resulting library of reusable components enjoys a clear theoretical basis and provides robust support for reuse. In the thesis I illustrate the approach in the area of parametric design
Logic programming in the context of multiparadigm programming: the Oz experience
Oz is a multiparadigm language that supports logic programming as one of its
major paradigms. A multiparadigm language is designed to support different
programming paradigms (logic, functional, constraint, object-oriented,
sequential, concurrent, etc.) with equal ease. This article has two goals: to
give a tutorial of logic programming in Oz and to show how logic programming
fits naturally into the wider context of multiparadigm programming. Our
experience shows that there are two classes of problems, which we call
algorithmic and search problems, for which logic programming can help formulate
practical solutions. Algorithmic problems have known efficient algorithms.
Search problems do not have known efficient algorithms but can be solved with
search. The Oz support for logic programming targets these two problem classes
specifically, using the concepts needed for each. This is in contrast to the
Prolog approach, which targets both classes with one set of concepts, which
results in less than optimal support for each class. To explain the essential
difference between algorithmic and search programs, we define the Oz execution
model. This model subsumes both concurrent logic programming
(committed-choice-style) and search-based logic programming (Prolog-style).
Instead of Horn clause syntax, Oz has a simple, fully compositional,
higher-order syntax that accommodates the abilities of the language. We
conclude with lessons learned from this work, a brief history of Oz, and many
entry points into the Oz literature.Comment: 48 pages, to appear in the journal "Theory and Practice of Logic
Programming
Applying the proto-theory of design to explain and modify the parameter analysis method of conceptual design
This article reports on the outcomes of applying the notions provided by the reconstructed proto-theory of design, based on Aristotle’s remarks, to the parameter analysis (PA) method of conceptual design. Two research questions are addressed: (1) What further clarification and explanation to the approach of PA is provided by the proto-theory? (2) Which conclusions can be drawn from the study of an empirically derived
design approach through the proto-theory regarding usefulness, validity and range of that theory? An overview of PA and an application example illustrate its present model and unique characteristics. Then, seven features of the proto-theory are explained and demonstrated through geometrical problem solving and analogies are drawn between these features and the corresponding ideas in modern design thinking.
Historical and current uses of the terms analysis and synthesis in design are also outlined and contrasted, showing that caution should be exercised when applying them. Consequences regarding the design moves, process and strategy of PA allow proposing modifications to its model, while demonstrating how the ancient method of analysis can contribute to better understanding of contemporary design-theoretic issues
Optimization Modulo Theories with Linear Rational Costs
In the contexts of automated reasoning (AR) and formal verification (FV),
important decision problems are effectively encoded into Satisfiability Modulo
Theories (SMT). In the last decade efficient SMT solvers have been developed
for several theories of practical interest (e.g., linear arithmetic, arrays,
bit-vectors). Surprisingly, little work has been done to extend SMT to deal
with optimization problems; in particular, we are not aware of any previous
work on SMT solvers able to produce solutions which minimize cost functions
over arithmetical variables. This is unfortunate, since some problems of
interest require this functionality.
In the work described in this paper we start filling this gap. We present and
discuss two general procedures for leveraging SMT to handle the minimization of
linear rational cost functions, combining SMT with standard minimization
techniques. We have implemented the procedures within the MathSAT SMT solver.
Due to the absence of competitors in the AR, FV and SMT domains, we have
experimentally evaluated our implementation against state-of-the-art tools for
the domain of linear generalized disjunctive programming (LGDP), which is
closest in spirit to our domain, on sets of problems which have been previously
proposed as benchmarks for the latter tools. The results show that our tool is
very competitive with, and often outperforms, these tools on these problems,
clearly demonstrating the potential of the approach.Comment: Submitted on january 2014 to ACM Transactions on Computational Logic,
currently under revision. arXiv admin note: text overlap with arXiv:1202.140
Probabilistic Plan Synthesis for Coupled Multi-Agent Systems
This paper presents a fully automated procedure for controller synthesis for
multi-agent systems under the presence of uncertainties. We model the motion of
each of the agents in the environment as a Markov Decision Process (MDP)
and we assign to each agent one individual high-level formula given in
Probabilistic Computational Tree Logic (PCTL). Each agent may need to
collaborate with other agents in order to achieve a task. The collaboration is
imposed by sharing actions between the agents. We aim to design local control
policies such that each agent satisfies its individual PCTL formula. The
proposed algorithm builds on clustering the agents, MDP products construction
and controller policies design. We show that our approach has better
computational complexity than the centralized case, which traditionally suffers
from very high computational demands.Comment: IFAC WC 2017, Toulouse, Franc
The Epistemology of scheduling problems
Scheduling is a knowledge-intensive task spanning over many activities in day-to-day life. It deals with the temporally-bound assignment of jobs to resources. Although scheduling has been extensively researched in the AI community for the past 30 years, efforts have primarily focused on specific applications, algorithms, or 'scheduling shells' and no comprehensive analysis exists on the nature of scheduling problems, which provides a formal account of what scheduling is, independently of the way scheduling problems can be approached. Research on KBS development by reuse makes use of ontologies, to provide knowledge-level specifications of reusable KBS components. In this paper we describe a task ontology, which formally characterises the nature of scheduling problems, independently of particular application domains and in-dependently of how the problems can be solved. Our results provide a comprehensive, domain-independent and formally specified refer-ence model for scheduling applications. This can be used as the ba-sis for further analyses of the class of scheduling problems and also as a concrete reusable resource to support knowledge acquisition and system development in scheduling applications
A new model for solution of complex distributed constrained problems
In this paper we describe an original computational model for solving
different types of Distributed Constraint Satisfaction Problems (DCSP). The
proposed model is called Controller-Agents for Constraints Solving (CACS). This
model is intended to be used which is an emerged field from the integration
between two paradigms of different nature: Multi-Agent Systems (MAS) and the
Constraint Satisfaction Problem paradigm (CSP) where all constraints are
treated in central manner as a black-box. This model allows grouping
constraints to form a subset that will be treated together as a local problem
inside the controller. Using this model allows also handling non-binary
constraints easily and directly so that no translating of constraints into
binary ones is needed. This paper presents the implementation outlines of a
prototype of DCSP solver, its usage methodology and overview of the CACS
application for timetabling problems
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