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Arbitrary Announcements in Propositional Belief Revision
Arbitrary Announcements in Propositional Belief Revision

... formulas in the usual way. The study of belief change is concerned with the way that rational agents incorporate new information. One important form of belief change is belief revision, which is the change that occurs when the new information may be inconsistent with some of the original beliefs. On ...
CS 561a: Introduction to Artificial Intelligence
CS 561a: Introduction to Artificial Intelligence

... • One is biological, based on the idea that since humans are intelligent, AI should study humans and imitate their psychology or physiology. • The other is phenomenal, based on studying and formalizing common sense facts about the world and the problems that the world presents to the achievement of ...
A Glimpse on Gerhard Brewka`s Contributions to Artificial Intelligence
A Glimpse on Gerhard Brewka`s Contributions to Artificial Intelligence

... rules of order, and also allowed meta-argumentation about these rules (and meta-meta-argumentation up to arbitrary depth). Together with Steven Shapiro, Gerd also investigated dynamic interactions between goals and beliefs [78]. It was standard practice in the agent/planning literature that an achie ...
i S dS i S dS Fuzzy Logic, Sets and Systems Lecture 1 Introduction
i S dS i S dS Fuzzy Logic, Sets and Systems Lecture 1 Introduction

...  Based on intuition and judgment  No need for a mathematical model  Provides a smooth transition between members and nonmembers  Relatively simple, fast and adaptive  Less sensitive to system fluctuations  Can implement design objectives, difficult to express mathematicall in linguistic ...
Knowledge representation and propositional logic
Knowledge representation and propositional logic

... – A way of drawing conclusions from what we already know.  We ...
Complex Preferences for Answer Set Optimization
Complex Preferences for Answer Set Optimization

... We then demonstrate how several preference handling methods described in the literature can be expressed using   , and we discuss complexity issues. Finally, we show how optimal answer sets can be computed on top of a standard answer set solver using a generate and improve strategy. For this purp ...
Artificial Intelligence
Artificial Intelligence

...  Lisp is a family of computer programming languages with a long history and a distinctive, fully parenthesized Polish prefix notation.  The name LISP derives from "List Processing". Linked lists are one of Lisp languages' major data structures, and Lisp source code is itself made up of lists. As a ...
A suitable semantics for implicit and explicit belief
A suitable semantics for implicit and explicit belief

... it typically modelled on normal frames for epistemic logic as a K45 or a KD45 modality, whereas different conditions imposed on the set of propositions of which the agents are aware allow us to capture various interpretations of explicit belief. In spite of its merits, this approach is not fully apt ...
How an Agent Might Think
How an Agent Might Think

... These syntactic changes are accompanied by new definitions of semantics. We have also provided a new PT IME algorithm for computing well-supported models and working with such syntactic extensions. Let us emphasize that the proposed semantics is rather general as, in its substantial part, it abstrac ...
Software Engineering and Automated Deduction
Software Engineering and Automated Deduction

... Many software engineering tasks depend critically on the ability to solve logic problems. We enumerate a few examples: Semantics: What is the semantics of signed twos-complement representation of integers? An N-bit word or bit-vector can be represented as a function from the subrange interval [0, N) ...
3_types
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... In memory, everything is just bits; type is what gives meaning to the bits (bits/binary) 01100001 is the int 97 is the char 'a' (bits/binary) 01000001 is the int 65 is the char 'A' (bits/binary) 00110000 is the int 48 is the char '0' char c = 'a'; cout << c; // print the value of character c, which ...
PhD Thesis in Cognitive Science
PhD Thesis in Cognitive Science

... his influential essay “Computing machinery and intelligence” [12]. With programmable machines, algorithms were designed that could perform some of the mental tasks that require higher mental abilities such as reasoning and learning. The study and design of such algorithms and methods form the basis ...
Guess My Number game
Guess My Number game

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FLAIRS-30 Uncertain Reasoning Track
FLAIRS-30 Uncertain Reasoning Track

... • Conditional logics, Description logic, Logic programming ...
CS 561a: Introduction to Artificial Intelligence
CS 561a: Introduction to Artificial Intelligence

... • One is biological, based on the idea that since humans are intelligent, AI should study humans and imitate their psychology or physiology. • The other is phenomenal, based on studying and formalizing common sense facts about the world and the problems that the world presents to the achievement of ...
The Fourth International Workshop on Nonmonotonic Reasoning
The Fourth International Workshop on Nonmonotonic Reasoning

... reasoning, including semantics, representational issues, and particular formalisms. Robert Stalnaker (Massachusetts Institute of Technology [MIT]) presented a philosophical study of the whole question of what a nonmonotonic consequence relation is, discussing the conditions that such a relation shou ...
PDF
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... For many logics with counting quantifiers, the complexity of the satisfiability problem is known for unary coding only, and is unknown for binary coding. For example, !  (twovariable first-order logic with counting) is known to be NE XP T IME-complete if numbers in counting quantifiers are coded in ...
DSTO-TR-2324 PR
DSTO-TR-2324 PR

... representation languages and reasoning systems with the aim of deciding upon the best approach to our information fusion task. We stress that this report in no way gives a complete overview of the field. There is much ground that we do not cover, some of which is discussed briefly in Section 9, and ...
Consistent Belief Reasoning in the Presence of Inconsistency
Consistent Belief Reasoning in the Presence of Inconsistency

... As mentioned, in real life a knowledge base may be inconsistent. This can easily occur in a medical expert system whose knowledge is obtained from multiple physician experts, where it is common that different experts hold conflicting views on their domain of expertise. Then the question is: given th ...
Distributed Resolution for Expressive Ontology Networks
Distributed Resolution for Expressive Ontology Networks

... these options have been investigated in the field of automated theorem proving for firstorder logics [4, 3]. In the following we discuss these options and their pros and cons with respect to the requirements and goals defined in the introduction. In particular, we have to make two choices: 1. We hav ...
A Framework for Decision-Theoretic Planning 1: Combining
A Framework for Decision-Theoretic Planning 1: Combining

... ity depending on the state it ends up in. This is done to simplify the formalism, and seems natural. This does not mean that we cannot model cases where an agent receives rewards and costs along the way, but the re­ wards accumulated then have to be part of the state. Note that MOPs also need to mak ...
Advancing Multi-Context Systems by Inconsistency Management
Advancing Multi-Context Systems by Inconsistency Management

... a transformation of the MCS and slight adaption of the notion of diagnosis is sufficient to achieve the desired effects in [13]. As one of the strengths of MCS is the ability to allow arbitrary formalisms for knowledge representation inside contexts, we do not want to restrict the users to a specifi ...
Automated Modelling and Solving in Constraint Programming
Automated Modelling and Solving in Constraint Programming

... set, which is given, for instance, as a set of examples of its solutions and non-solutions. This kind of learning is called constraint acquisition (Bessiere et al. 2005). The motivations for constraint acquisition are many. For example, in order to solve partially defined constraints more efficient ...
SIP APPLICATION SERVERS & WeSIP
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Bayesian Networks for Logical Reasoning
Bayesian Networks for Logical Reasoning

... and mathematical propositions are constantly being evaluated on the basis of the mathematical and physical evidence available at the time.1 Of course logical reasoning has many other applications, notably throughout the field of artificial intelligence. Planning a decision, parsing a sentence, query ...
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Logic programming

Logic programming is a programming paradigm based on formal logic. A program written in a logic programming language is a set of sentences in logical form, expressing facts and rules about some problem domain. Major logic programming language families include Prolog, Answer set programming (ASP) and Datalog. In all of these languages, rules are written in the form of clauses:H :- B1, …, Bn.and are read declaratively as logical implications:H if B1 and … and Bn.H is called the head of the rule and B1, …, Bn is called the body. Facts are rules that have no body, and are written in the simplified form:H.In the simplest case in which H, B1, …, Bn are all atomic formulae, these clauses are called definite clauses or Horn clauses. However, there exist many extensions of this simple case, the most important one being the case in which conditions in the body of a clause can also be negations of atomic formulae. Logic programming languages that include this extension have the knowledge representation capabilities of a non-monotonic logic.In ASP and Datalog, logic programs have only a declarative reading, and their execution is performed by means of a proof procedure or model generator whose behaviour is not meant to be under the control of the programmer. However, in the Prolog family of languages, logic programs also have a procedural interpretation as goal-reduction procedures:to solve H, solve B1, and ... and solve Bn.Consider, for example, the following clause:fallible(X) :- human(X).based on an example used by Terry Winograd to illustrate the programming language Planner. As a clause in a logic program, it can be used both as a procedure to test whether X is fallible by testing whether X is human, and as a procedure to find an X that is fallible by finding an X that is human. Even facts have a procedural interpretation. For example, the clause:human(socrates).can be used both as a procedure to show that socrates is human, and as a procedure to find an X that is human by ""assigning"" socrates to X.The declarative reading of logic programs can be used by a programmer to verify their correctness. Moreover, logic-based program transformation techniques can also be used to transform logic programs into logically equivalent programs that are more efficient. In the Prolog family of logic programming languages, the programmer can also use the known problem-solving behaviour of the execution mechanism to improve the efficiency of programs.
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