Perception and reasoning are two representative abilities of intelligence that are integrated seamlessly during human problem-solving processes. In the area of artificial intelligence (AI), the two abilities are usual...
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Perception and reasoning are two representative abilities of intelligence that are integrated seamlessly during human problem-solving processes. In the area of artificial intelligence (AI), the two abilities are usually realised by machine learning and logic programming, respectively. However, the two categories of techniques were developed separately throughout most of the history of AI. In this paper, we present the abductive learning targeted at unifying the two AI paradigms in a mutually beneficial way, where the machine learning model learns to perceive primitive logic facts from data, while logical reasoning can exploit symbolic domain knowledge and correct the wrongly perceived facts for improving the machine learning models. Furthermore, we propose a novel approach to optimise the machine learning model and the logical reasoning model jointly. We demonstrate that by using abductive learning, machines can learn to recognise numbers and resolve unknown mathematical operations simultaneously from images of simple hand-written equations. Moreover, the learned models can be generalised to longer equations and adapted to different tasks, which is beyond the capability of state-of-the-art deep learning models.
It is important to understand how the outcome of an election can be modified by an agent with control over the structure of the election. Electoral control has been studied for many election systems, but for all these...
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ISBN:
(纸本)9781577358091
It is important to understand how the outcome of an election can be modified by an agent with control over the structure of the election. Electoral control has been studied for many election systems, but for all these systems the winner problem is in P, and so control is in NP. There are election systems, such as Kemeny, that have many desirable properties, but whose winner problems are not in NP. Thus for such systems control is not in NP, and in fact we show that it is typically complete for Sigma(p)(2) (i.e., NPNP, the second level of the polynomial hierarchy). This is a very high level of complexity. Approaches that perform quite well for solving NP problems do not necessarily work for Sigma(p)(2)-complete problems. However, answer set programming is suited to express problems in Sigma(p)(2), and we present an encoding for Kemeny control.
Today Cognitive computing and Artificial Intelligence (AI) face the same challenges namely, simulate human thought processes and mimic the way human brain works. The main difference between Cognitive computing and AI ...
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ISBN:
(纸本)9783030332747;9783030332730
Today Cognitive computing and Artificial Intelligence (AI) face the same challenges namely, simulate human thought processes and mimic the way human brain works. The main difference between Cognitive computing and AI is: (i) AI models various functions of human intelligence, where computer is one of the modelling means though often the most important one, i.e. intelligence is in the focus while (ii) Cognitive computing models human thought processes and simulates the hypothetical way human brain works as computation. Our aim is to develop a theoretically and methodologically well-founded theory of AI together with a unified computational theory, which will provide specific tools and methods for Cognitive computing. To achieve our goal we follow a methodology triangle, consisting of a conceptual-philosophical, a system theoretical and a logical-mathematical component. Computing will play a fundamental role in both system-theoretical and logical-mathematical methodological components. Hereby we concentrate on the development of the logical-mathematical foundation in detail by the use of category theory, which provides an excellent frame for defining all notions necessary for developing a universal theory for computing, specification, cognitive reasoning, information, knowledge and their various combinations. Foundation theory is by the use of the so-called constitutions, the mathematical basis for the cognitive computation. logical foundation will be developed as a special constitution and cognitive computing processes are defined by using situations, infons and information. The main properties are discussed with some examples.
Although parity constraints are at the heart of many relevant reasoning modes like sampling or model counting, little attention has so far been paid to their integration into ASP systems. We address this shortcoming a...
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ISBN:
(纸本)9783030205287;9783030205270
Although parity constraints are at the heart of many relevant reasoning modes like sampling or model counting, little attention has so far been paid to their integration into ASP systems. We address this shortcoming and investigate a variety of alternative approaches to implementing parity constraints, ranging from rather basic ASP encodings to more sophisticated theory propagators (featuring Gauss-Jordan elimination). All of them are implemented in the xorro system by building on the theory reasoning capabilities of the ASP system dingo. Our comparative empirical study investigates the impact of the number and size of parity constraints on performance and indicates the merits of the respective implementation techniques. Finally, we benefit from parity constraints to equip xorro with means to sample answer sets, paving the way for new applications of ASP.
Assumption-based argumentation is one of the most prominent formalisms for logical (or structured) argumentation. It has been shown useful for representing defeasible reasoning and has tight links to logic programming...
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ISBN:
(纸本)9783030205287;9783030205270
Assumption-based argumentation is one of the most prominent formalisms for logical (or structured) argumentation. It has been shown useful for representing defeasible reasoning and has tight links to logic programming In this paper we study the Dung semantics for extended forms of assumption-based argumentation frameworks (ABFs), based on any contrapositive propositional logic, and whose defeasible rules are expressed by arbitrary formulas in that logic. In particular, new results on the well-founded semantics for such ABFs are reported, the redundancy of the closure condition is shown, and the use of disjunctive attacks is investigated. Finally, some useful properties of the generalized frameworks are considered.
Intentional forgetting means to deliberately give up information and is a crucial part of change or consolidation processes, or to make knowledge more compact. Two well-known forgetting operations are contraction in t...
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ISBN:
(纸本)9783030299088;9783030299071
Intentional forgetting means to deliberately give up information and is a crucial part of change or consolidation processes, or to make knowledge more compact. Two well-known forgetting operations are contraction in the AGM theory of belief change, and various types of variable elimination in logic programming. While previous work dealt with postulates being inspired from logic programming, in this paper we focus on evaluating forgetting in epistemic states according to postulates coming from AGM belief change theory. We consider different forms of contraction, marginalization, and conditionalization as major representatives of forgetting operators to be evaluated. We use Spohn's ranking functions as a common semantic base to show that all operations can be realized in one logical framework, thereby exploring the richness of forgetting operations in a comparable way.
In this thesis, we introduce a novel formal framework to represent and reason about qualitative di-rection and distance relations between extended objects using Answer Set programming (ASP). We take Cardinal Direction...
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In this thesis, we introduce a novel formal framework to represent and reason about qualitative di-rection and distance relations between extended objects using Answer Set programming (ASP). We take Cardinal Directional Calculus (CDC) as a starting point and extend CDC with new sorts of con-straints which involve defaults, preferences and negation. We call this extended version as nCDC. Then we further extend nCDC by augmenting qualitative distance relation and name this extension as nCDC+. For CDC, nCDC, nCDC+, we introduce an ASP-based general framework to solve con-sistency checking problems, address composition and inversion of qualitative spatial relations, infer unknown or missing relations between objects, and find a suitable configuration of objects which fulfills a given inquiry.
In order to effectively implement guidance structures in a computational social system, directives which are specified in general terms of duties and rights need to be transformed in terms of powers and liabilities at...
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ISBN:
(纸本)9781643680491;9781643680484
In order to effectively implement guidance structures in a computational social system, directives which are specified in general terms of duties and rights need to be transformed in terms of powers and liabilities attributed to social parties. The present paper is a work in progress report on an axiomatization of power structures in a logic programming setting, covering the intentional level in specifying actions, the connection between productive characterization of actions and causation, the default nature of action specifications, failures and omissions, the relations of causation and power, and the concept of interfering actions.
There is a consensus on integrating computing with STEM teaching in K-12. However, very little is known about the integration. In this paper, we propose a novel framework for integrating science and computational thin...
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ISBN:
(纸本)9781450362597
There is a consensus on integrating computing with STEM teaching in K-12. However, very little is known about the integration. In this paper, we propose a novel framework for integrating science and computational thinking teaching using logic programming. We then develop and implement two 8-session integration modules on chemistry and physics for 6th and 7th graders. Pre-and post-tests, class observations and interviews show the feasibility of the framework in terms of 1) development and implementation of the modules, and 2) the students' learning outcomes on science content and Computational Thinking, and their acceptance of the integration.
Property-based testing (PBT) is a technique for validating code against an executable specification by automatically generating test-data. We present a proof-theoretical reconstruction of this style of testing for rel...
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ISBN:
(纸本)9781450372497
Property-based testing (PBT) is a technique for validating code against an executable specification by automatically generating test-data. We present a proof-theoretical reconstruction of this style of testing for relational specifications and employ the Foundational Proof Certificate framework to describe test generators. We do this by presenting certain kinds of "proof outlines" that can be used to describe various common generation strategies in the PBT literature, ranging from random to exhaustive, including their combination. We also address the shrinking of counterexamples as a first step towards their explanation. Once generation is accomplished, the testing phase boils down to a standard logic programming search. After illustrating our techniques on simple, first-order (algebraic) data structures, we lift it to data structures containing bindings using.-tree syntax. The lambda Prolog programming language is capable of performing both the generation and checking of tests. We validate this approach by tackling benchmarks in the metatheory of programming languages coming from related tools such as PLT-Redex.
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