In this paper we will discuss the context management features of the new logicprogramming language DALI, aimed at defining agents and multi-agent systems. In particular, a DALI agent, which is capable of reactive and...
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ISBN:
(纸本)3540403809
In this paper we will discuss the context management features of the new logicprogramming language DALI, aimed at defining agents and multi-agent systems. In particular, a DALI agent, which is capable of reactive and proactive behaviour, builds step-by-step her context. Context update is modelled by the novel concept of "evolutionary semantics", where each context manipulation is interpreted as a program transformation step. We show that this kind of context-based agent language is well-suited for representing many significant commonsense reasoning examples.
this paper concerns formal theories for reasoning about the knowledge and belief of agents. It has seemed attractive to researchers in artificialintelligence to formalise these propositional attitudes as predicates o...
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this paper concerns formal theories for reasoning about the knowledge and belief of agents. It has seemed attractive to researchers in artificialintelligence to formalise these propositional attitudes as predicates of first-order predicate logic. this allows the agents to express stronger introspective beliefs and engage in stronger meta-rcasoning than in the classical modal operator approach. Results by Montague [1963] and thomason [1980] show, however, that the predicate approach is prone to inconsistency. More recent results by des Rivieres & Levesque [1988] and Morreau & Kraus [1998] show that we can maintain the predicate approach if we make suitable restrictions to our set of epistemic axioms. their results are proved by careful translations from corresponding modal formalisms. In the present paper we show that their results fit nicely into the framework of logicprogramming semantics, in that we show their results to be corollaries of well-known results in this field. this does not only allow us to demonstrate a close connection between consistency problems in the syntactic treatment of propositional attitudes and problems in semantics for logic programs, but it also allows us to strengthen the results of des Rivieres & Levesque [1988] and Morreau & Kraus [1998].
We investigate the problem of generalizing acceleration techniques as found in recent, satisfiability engines for conjunctive normal forms (CNFs) to linear constraint systems over the Booleans. the rationale behind th...
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ISBN:
(纸本)3540201017
We investigate the problem of generalizing acceleration techniques as found in recent, satisfiability engines for conjunctive normal forms (CNFs) to linear constraint systems over the Booleans. the rationale behind this research is that rewriting the propositional formulae occurring in e.g. bounded model checking (BMC) [5] to CNF requires a blowup in either the formula size (worst-case exponential) or in the number of propositional variables (linear, thus yielding a worst-case exponential blow-up of the search space). We demonstrate that acceleration techniques like observation lists and lazy clause evaluation [14] as well as the more traditional non-chronological backtracking and learning techniques generalize smoothly to Davis-Putnam-like resolution procedures for the very concise propositional logic of linear constraint systems over the Booleans. Despite the more expressive input language, the performance of our prototype implementation comes surprisingly close to that of state-of-the-art CNF-SAT engines like ZChaff [14]. First experiments with bounded model-construction problems show that the overhead in the satisfiability engine that can be attributed to the richer input language is often amortized by the conciseness gained in the propositional encoding of the BMC problem.
We often reach conclusions partially on the basis that we do not have evidence that the conclusion is false. A newspaper story warning that the local water supply has been contaminated would prevent a person from drin...
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ISBN:
(纸本)354000680X
We often reach conclusions partially on the basis that we do not have evidence that the conclusion is false. A newspaper story warning that the local water supply has been contaminated would prevent a person from drinking water from the tap in her home. this suggests that the absence of such evidence contributes to her usual belief that her water is safe. On the other hand, if a reasonable person received a letter telling her that she had won a million dollars, she would consciously consider whether there was any evidence that the letter was a hoax or somehow misleading before making plans to spend the money. All to often we arrive at conclusions which we later retract when contrary evidence becomes available. the contrary evidence defeats our earlier reasoning. Much of our reasoning is defeasible in this way. Since around 1980, considerable research in AI has focused on how to model reasoning of this sort. In this paper, I describe one theoretical approach to this problem, discuss implementation of this approach as an extension of Prolog, and describe some application of this work to normative reasoning, learning, planning, and other types of automated reasoning.
We believe that AI programs written for discovery tasks will need to simultaneously employ a variety of reasoning techniques such as induction, abduction, deduction, calculation and invention. We describe the HR syste...
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ISBN:
(纸本)3540201440
We believe that AI programs written for discovery tasks will need to simultaneously employ a variety of reasoning techniques such as induction, abduction, deduction, calculation and invention. We describe the HR system which performs a novel ILP routine called automated theory formation. this combines inductive and deductive reasoning to form clausal theories consisting of classification rules and association rules. HR generates definitions using a set of production rules, interprets the definitions as classification rules, then uses the success sets of the definitions to induce hypotheses from which it extracts association rules. It uses third party theorem provers and model generators to check whether the association rules are entailed by a set of user supplied axioms. HR has been applied to a range of predictive, descriptive and subgroup discovery tasks in domains of pure mathematics. We describe these applications and how they have led to some interesting mathematical discoveries. Our main aim here is to provide a thorough overview of automated theory formation. A secondary aim is to promote mathematics as a worthy domain for ILP applications, and we provide pointers to mathematical datasets.
New representation languages that integrate first order logic with Bayesian networks have been proposed in the literature. Probabilistic Relational models (PRM) and Bayesian logic Programs (BLP) are examples. Algorith...
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ISBN:
(纸本)3540005676
New representation languages that integrate first order logic with Bayesian networks have been proposed in the literature. Probabilistic Relational models (PRM) and Bayesian logic Programs (BLP) are examples. Algorithms to learn boththe qualitative and the quantitative components of these languages have been developed. Recently, we have developed an algorithm to revise a BLP. In this paper, we discuss the relationship among these approaches, extend our revision algorithm to return the highest probabilistic scoring BLP and argue that for a classification task our approach, which uses techniques of theory revision and so searches a smaller hypotheses space, can be a more adequate choice.
An Event Calculus program to control the navigation of a real robot was generated using theory Completion techniques. this is an application of ILP in the non-observational predicate learning setting. this work utiliz...
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ISBN:
(纸本)3540005676
An Event Calculus program to control the navigation of a real robot was generated using theory Completion techniques. this is an application of ILP in the non-observational predicate learning setting. this work utilized 1) extraction-case abduction;2) the simultaneous completion of two, mutually related predicates;and 3) positive observations only learning. Given time-trace observations of a robot successfully navigating a model office and other background information, theory Completion was used to induce navigation control programs in the event calculus. Such programs consisted of many clauses (up to 15) in two mutually related predicates. this application demonstrates that abduction and induction can be combined to effect non-observational multi-predicate learning.
An approximation space (U, R) placed in a type-lowering retraction with 2(UxU) provides a model for a first order calculus of relations for computing over lists and reasoning about the resulting programs. Upper and lo...
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ISBN:
(纸本)3540140409
An approximation space (U, R) placed in a type-lowering retraction with 2(UxU) provides a model for a first order calculus of relations for computing over lists and reasoning about the resulting programs. Upper and lower approximations to the scheme of primitive recursion of the theory of Pairs are derived from the approximation operators of an abstract approximation space (U, lozenge : u right bar arrow U[U](R), square : u right bar arrow boolean AND[u](R)).
In this paper, we introduce an alternative approach to reasoning about action. the approach provides a solution to the frame and the ramification problem in a uniform manner. the approach involves keeping a (syntax-ba...
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We present a Markov chain Monte Carlo algorithm that operates on generic model structures that are represented by terms found in the computed answers produced by stochastic logic programs. the objective of this paper ...
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ISBN:
(纸本)354000680X
We present a Markov chain Monte Carlo algorithm that operates on generic model structures that are represented by terms found in the computed answers produced by stochastic logic programs. the objective of this paper is threefold (a) to show that SLD-trees are an elegant means for describing prior distributions over model structures (b) to sketch an implementation of the MCMC algorithm in Prolog, and (c) to provide insights on desirable properties for SLPs.
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