We propose a timed and soft extension of Concurrent Constraint Programming. The time extension is based on the hypothesis of bounded asynchrony: the computation takes a bounded period of time and is measured by a discrete global clock. Action prefixing is then considered as the syntactic marker which distinguishes a time instant from the next one. Supported by soft constraints instead of crisp ones, tell and ask agents are now equipped with a preference (or consistency) threshold which is used to determine their success or suspension. In the paper we provide a language to describe the agents behavior, together with its operational and denotational semantics, for which we also prove the compositionality and correctness properties. After presenting a semantics using maximal parallelism of actions, we also describe a version for their interleaving on a single processor (with maximal parallelism for time elapsing). Coordinating agents that need to take decisions both on preference values and time events may benefit from this language. To appear in Theory and Practice of Logic Programming (TPLP).
In literature we can find other proposals that are related to tuple-based kernel-languages instead of a constraint store, as @cite_24 () or @cite_18 () for instance. These languages are designed to study different properties of systems, as mobility and autonomicity of modeled agents. Their basic specification do not encompass time-based primitives, while mobility features are not present in any of the constraint-based languages reported in this section. The purpose of our language is to model systems where a level of preference and time-sensitive primitives (as a timeout) is required: a good example is represented by agents participating to an auction, as the example given in .
@cite_24 @cite_18
1987486061 2011124182 2111141597 2075969214
The availability of large scale data sets of manually annotated predicate-argument structures has recently favored the use of machine learning approaches to the design of automated semantic role labeling (SRL) systems. The main research in this area relates to the design choices for feature representation and for effective decompositions of the task in different learning models. Regarding the former choice, structural properties of full syntactic parses are largely employed as they represent ways to encode different principles suggested by the linking theory between syntax and semantics. The latter choice relates to several learning schemes over global views of the parses. For example, re-ranking stages operating over alternative predicate-argument sequences of the same sentence have shown to be very effective. In this article, we propose several kernel functions to model parse tree properties in kernel-based machines, for example, perceptrons or support vector machines. In particular, we define different kinds of tree kernels as general approaches to feature engineering in SRL. Moreover, we extensively experiment with such kernels to investigate their contribution to individual stages of an SRL architecture both in isolation and in combination with other traditional manually coded features. The results for boundary recognition, classification, and re-ranking stages provide systematic evidence about the significant impact of tree kernels on the overall accuracy, especially when the amount of training data is small. As a conclusive result, tree kernels allow for a general and easily portable feature engineering method which is applicable to a large family of natural language processing tasks. A novel logic program like language, weight constraint rules, is developed for answer set programming purposes. It generalizes normal logic programs by allowing weight constraints in place of literals to represent, e.g., cardinality and resource constraints and by providing optimization capabilities. A declarative semantics is developed which extends the stable model semantics of normal programs. The computational complexity of the language is shown to be similar to that of normal programs under the stable model semantics. A simple embedding of general weight constraint rules to a small subclass of the language called basic constraint rules is devised. An implementation of the language, the SMODELS system, is developed based on this embedding. It uses a two level architecture consisting of a front-end and a kernel language implementation. The front-end allows restricted use of variables and functions and compiles general weight constraint rules to basic constraint rules. A major part of the work is the development of an efficient search procedure for computing stable models for this kernel language. The procedure is compared with and empirically tested against satisfiability checkers and an implementation of the stable model semantics. It offers a competitive implementation of the stable model semantics for normal programs and attractive performance for problems where the new types of rules provide a compact representation. The mathematical foundations of a new theory for the design of intelligent agents are presented. The proposed learning paradigm is centered around the concept of constraint, representing the interactions with the environment, and the parsimony principle. The classical regularization framework of kernel machines is naturally extended to the case in which the agents interact with a richer environment, where abstract granules of knowledge, compactly described by different linguistic formalisms, can be translated into the unified notion of constraint for defining the hypothesis set. Constrained variational calculus is exploited to derive general representation theorems that provide a description of the optimal body of the agent i.e., the functional structure of the optimal solution to the learning problem, which is the basis for devising new learning algorithms. We show that regardless of the kind of constraints, the optimal body of the agent is a support constraint machine SCM based on representer theorems that extend classical results for kernel machines and provide new representations. In a sense, the expressiveness of constraints yields a semantic-based regularization theory, which strongly restricts the hypothesis set of classical regularization. Some guidelines to unify continuous and discrete computational mechanisms are given so as to accommodate in the same framework various kinds of stimuli, for example, supervised examples and logic predicates. The proposed view of learning from constraints incorporates classical learning from examples and extends naturally to the case in which the examples are subsets of the input space, which is related to learning propositional logic clauses. While model checking of pushdown systems is by now an established technique in software verification, temporal logics and automata traditionally used in this area are unattractive on two counts. First, logics and automata traditionally used in model checking cannot express requirements such as pre post-conditions that are basic to analysis of software. Second, unlike in the finite-state world, where the μ-calculus has a symbolic model-checking algorithm and serves as an “assembly language” to which temporal logics can be compiled, there is no common formalism—either fixpoint-based or automata-theoretic—to model-check requirements on pushdown models. In this article, we introduce a new theory of temporal logics and automata that addresses the above issues, and provides a unified foundation for the verification of pushdown systems. The key idea here is to view a program as a generator of structures known as nested trees as opposed to trees. A fixpoint logic (called N T -μ) and a class of automata (called nested tree automata) interpreted on languages of these structures are now defined, and branching-time model-checking is phrased as language inclusion and membership problems for these languages. We show that N T -μ and nested tree automata allow the specification of a new frontier of requirements usable in software verification. At the same time, their model checking problem has the same worst-case complexity as their traditional analogs, and can be solved symbolically using a fixpoint computation that generalizes, and includes as a special case, “summary”-based computations traditionally used in interprocedural program analysis. We also show that our logics and automata define a robust class of languages—in particular, just as the μ-calculus is equivalent to alternating parity automata on trees, NT-μ is equivalent to alternating parity automata on nested trees.