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We propose a framework grounded in Logic Programming for representing and reasoning about business processes from both the procedural and ontological point of views. In particular, our goal is threefold: (1) define a logical language and a formal semantics for process models enriched with ontology-based annotations; (2) provide an effective inference mechanism that supports the combination of reasoning services dealing with the structural definition of a process model, its behavior, and the domain knowledge related to the participating business entities; (3) implement such a theoretical framework into a process modeling and reasoning platform. To this end we define a process ontology coping with a relevant fragment of the popular BPMN modeling notation. The behavioral semantics of a process is defined as a state transition system by following an approach similar to the Fluent Calculus, and allows us to specify state change in terms of preconditions and effects of the enactment of activities. Then we show how the procedural process knowledge can be seamlessly integrated with the domain knowledge specified by using the OWL 2 RL rule-based ontology language. Our framework provides a wide range of reasoning services, including CTL model checking, which can be performed by using standard Logic Programming inference engines through a goal-oriented, efficient, sound and complete evaluation procedure. We also present a software environment implementing the proposed framework, and we report on an experimental evaluation of the system, whose results are encouraging and show the viability of the approach.

We solve a generalization of B\"uchi's problem in any exponent for function fields, and briefly discuss some consequences on undecidability. This provides the first example where this problem is solved for rings of functions in the case of an exponent larger than 3.

In former work, quantum computation has been shown to be a problem solving process essentially affected by both the reversible dynamics leading to the state before measurement, and the logical-mathematical constraints introduced by quantum measurement (in particular, the constraint that there is only one measurement outcome). This dual influence, originated by independent initial and final conditions, justifies the quantum computation speed-up and is not representable inside dynamics, namely as a one-way propagation. In this work, we reformulate von Neumann's model of quantum measurement at the light of above findings. We embed it in a broader representation based on the quantum logic gate formalism and capable of describing the interplay between dynamical and non-dynamical constraints. The two steps of the original model, namely (1) dynamically reaching a complete entanglement between pointer and quantum object and (2) enforcing the one-outcome-constraint, are unified and reversed. By representing step (2) right from the start, the same dynamics of step (1) yields a probability distribution of mutually exclusive measurement outcomes. This appears to be a more accurate and complete representation of quantum measurement. PACS: 03.67.-a, 03.67.Lx, 03.65.Bz

In former work, quantum computation has been shown to be a problem solving process essentially affected by both the reversible dynamics leading to the state before measurement, and the logical-mathematical constraints introduced by quantum measurement (in particular, the constraint that there is only one measurement outcome). This dual influence, originated by independent initial and final conditions, justifies the quantum computation speed-up and is not representable inside dynamics, namely as a one-way propagation. In this work, we reformulate von Neumann's model of quantum measurement at the light of above findings. We embed it in a broader representation based on the quantum logic gate formalism and capable of describing the interplay between dynamical and non-dynamical constraints. The two steps of the original model, namely (1) dynamically reaching a complete entanglement between pointer and quantum object and (2) enforcing the one-outcome-constraint, are unified and reversed. By representing step (2) right from the start, the same dynamics of step (1) yields a probability distribution of mutually exclusive measurement outcomes. This appears to be a more accurate and complete representation of quantum measurement. PACS: 03.67.-a, 03.67.Lx, 03.65.Bz

Complex problem solving in applied settings within the realm of IT security is an emergent field of psychological research. We propose an experimental study design on de-camouflaging, a task that is routinely performed during hardware reverse engineering. This task consists of determining the identity of camouflaged gates within Boolean networks that are purposely designed to have an identical appearance despite their different functions. In an experimental study manipulating representation as an experimental factor and investigating expertise as a quasi-experimental factor, we aim to understand whether problem solving is facilitated by a depictive compared to a descriptive representation.

This paper examines the role that formal logic ought to play in representing and reasoning with commonsense knowledge, We take issue with the commonly held view (as expressed by Newell [1980)) that the use of representations based on formal logic is inappropriate in most applications of artificial intelligence. We argue to the contrary that there is an important set of issues, involving incomplete knowledge of a problem situation, that so far have been addressed only by systems b)ased on formal logic and deductive inference, and that, in some sense, probably can be dealt with only by systems based on logic and deduction. We further argue that the experiments of the late l960s on problem- solving by theorem-proving did not show that the use of logic and deduction in AI systems was necessarily inefficient, but rather that what was needed was better control of the deduction process, combined with more attention to the computational properties of axioms.

Many logic programming based approaches can be used to describe and solve combinatorial search problems. On the one hand there are definite programs and constraint logic programs that compute a solution as an answer substitution to a query containing the variables of the constraint satisfaction problem. On the other hand there are approaches based on stable model semantics, abduction, and first-order logic model generation that compute solutions as models of some theory. This paper compares these different approaches from point of view of knowledge representation (how declarative are the programs) and from point of view of performance (how good are they at solving typical problems).

Many logic programming based approaches can be used to describe and solve combinatorial search problems. On the one hand there are definite programs and constraint logic programs that compute a solution as an answer substitution to a query containing the variables of the constraint satisfaction problem. On the other hand there are approaches based on stable model semantics, abduction, and first-order logic model generation that compute solutions as models of some theory. This paper compares these different approaches from point of view of knowledge representation (how declarative are the programs) and from point of view of performance (how good are they at solving typical problems).

This paper studies knowledge representation in multi-agent environment. We investigate technique for computation truth-values of statements based at a new temporal, agent's-knowledge logic TL. A logical language, mathematical symbolic models and a temporal logic TL based at these models are suggested. We find an algorithm which computes theorems of TL and satisfiability of statements, this implies that TL is decidable (i.e. -- the satisfiability problem for TL is solvable). Application areas are pointed and discussed.

This paper studies knowledge representation in multi-agent environment. We investigate technique for computation truth-values of statements based at a new temporal, agent's-knowledge logic TL. A logical language, mathematical symbolic models and a temporal logic TL based at these models are suggested. We find an algorithm which computes theorems of TL and satisfiability of statements, this implies that TL is decidable (i.e. -- the satisfiability problem for TL is solvable). Application areas are pointed and discussed.

This paper studies knowledge representation in multi-agent environment. We investigate technique for computation truth-values of statements based at a new temporal, agent's-knowledge logic TL. A logical language, mathematical symbolic models and a temporal logic TL based at these models are suggested. We find an algorithm which computes theorems of TL and satisfiability of statements, this implies that TL is decidable (i.e. -- the satisfiability problem for TL is solvable). Application areas are pointed and discussed.