Drone based terrorist attacks are increasing daily. It is not expected to be long before drones are used to carry out terror attacks in urban areas. We have developed the DUCK multi-agent testbed that security agencies can use to simulate drone-based attacks by diverse actors and develop a combination of surveillance camera, drone, and cyber defenses against them.

The eight papers in this special section focus on the state-of-the-art approaches, solutions, and applications in the area of rule representation, reasoning, and interchange in the context of distributed, (partially) open, heterogeneous environments, such as the semantic Web, intelligent multiagent systems, event-driven architectures. and service-oriented computing.

To date, there has been no work on temporal probabilistic agent reasoning on top of heterogeneous legacy databases and software modules. We will define the concept of aheterogeneous temporal probabilistic(HTP) agent. Such agents can be built on top of existing databases, data structures, and software code bases without explicitly accessing the internal code of those systems and can take actions compatible with a policy or operating principles specified by an agent developer. We will develop a formal semantics for such agents through the notion of a feasible temporal probabilistic status interpretation (FTPSI for short). Intuitively, an FTPSI specifies what all an HTP agent is permitted/forbidden/obliged to do at various timest. As changes occur in the environment, the HTP agent must compute a new FTPSI. HTP agents continuously compute FTPSIs in order to determine what they should do and, hence, the problem of computing FTPSIs is very important. We give a sound and complete algorithm to compute FTPSIs for a very large class of HTP agents calledstrictHTP agents. In a given state, many FTPSIs may exist. These represent alternative courses of action that the HTP agent can take. We provide a notion of anoptimalFTPSI that selects an FTPSI optimizing an objective function and give a sound and complete algorithm to compute an optimal FTPSI.

The Autoepistemic Logic of Knowledge and Belief (AELB) is a powerful nonmonotonic formalism introduced by Teodor Przymusinski in 1994. In this paper, we specialize it to a class of theories called "super logic programs". We argue that these programs form a natural generalization of standard logic programs. In particular, they allow disjunctions and default negation of arbitrary positive objective formulas.Our main results are two new and important characterizations of the static semantics of these programs, one syntactic, and one model-theoretic. The syntactic fixed point characterization is much simpler than the fixed point construction of the static semantics for arbitrary AELB theories. The model-theoretic characterization via Kripke models allows one to construct finite representations of the inherently infinite static expansions.Both characterizations can be used as the basis of algorithms for query answering under the static semantics. We describe a query-answering interpreter for super programs that we developed based on the model-theoretic characterization and which is available on the web.

We present a framework for expressing bottom-up algorithms to compute the well-founded model of non-disjunctive logic programs. Our method is based on the notion of conditional facts and elementary program transformations studied by BRASS and DIX (Brass and Dix, 1994; Brass and Dix, 1999) for disjunctive programs. However, even if we restrict their framework to nondisjunctive programs, their ‘residual program’ can grow to exponential size, whereas for function-free programs our ‘program remainder’ is always polynomial in the size of the extensional database (EDB). We show that particular orderings of our transformations (we call them strategies) correspond to well-known computational methods like the alternating fixpoint approach (Van Gelder, 1989; Van Gelder, 1993), the well-founded magic sets method (Kemp et al., 1995) and the magic alternating fixpoint procedure (Morishita, 1996). However, due to the confluence of our calculi (first noted in Brass and Dix, 1998), we come up with computations of the well-founded model that are provably better than these methods. In contrast to other approaches, our transformation method treats magic set transformed programs correctly, i.e. it always computes a relevant part of the well-founded model of the original program. These results show that our approach is a valuable tool to analyze, compare, and optimize existing evaluation methods or to create new strategies that are automatically proven to be correct if they can be described by a sequence of transformations in our framework. We have also developed a prototypical implementation. Experiments illustrate that the theoretical results carry over to the implemented prototype and may be used to optimize real life systems.

Agents are small programs that autonomously take actions based on changes in their environment or “state”. Over the last few years, there has been an increasing number of efforts to build agents that can interact and/or collaborate with other agents. In one of these efforts Eiter et al. [1999] have shown how agents may be built on top of legacy code. However, their framework assumes that agent states are completely determined, and there is no uncertainty in an agent's state. Thus, their framework allows an agent developer to specify how his agents will react when the agent is 100% sure about what is true/false in the world state. In this paper, we propose the concept of a probabilistic agent program and show how, given an arbitrary program written in any imperative language, we may build a declarative “probabilistic” agent program on top of it which supports decision making in the presence of uncertainty. We provide two alternative semantics for probabilitic programs. We provide sound and complete algorithms to compute the semantics of positive agent programs.

It is wellknown that Minker's semantics GCWA for positive disjunctive programs P, i.e. to decide if a literal is true in all minimal models of P is ΠP2 -complete. This is in contrast to the same entailment problem for semantics of non-disjunctive pro

Our aim in this article is to present a method for classifying and characterizing the various different semantics of logic programs with negation that have been considered in the last years. Instead of appealing to more or less questionable intuitions, we take a more structural view: our starting point is the observation that all semantics induce in a natural way non-monotonic entailment relations “ |˜ ”. The novel idea of our approach is to ask for the properties of these |˜ -relations and to use them for describing all possible semantics. The main properties discussed in this paper are adaptations of rules that play a fundamental rôle in general non-monotonic reasoning: Cumulativity and Rationality. They were introduced and investigated by Gabbay, Kraus, Lehmann, Magidor and Makinson. We show that the 3-valued version COMP 3 of Clark's completion, the stratified semantics M supp P as well as the well-founded semantics WFS and two extensions of it behave very regular: they are cumulative, rational and one of them is even supraclassical. While Pereira's recently proposed semantics O-SEM is not rational it is still cumulative. Cumulativity fails for the regular semantics REG-SEM of You/Yuan (recently shown to be equivalent to three other proposals). In a second article we will supplement these strong rules with a set of weak rules and consider the problem of uniquely describing a given semantics by its strong and weak properties together.

Our aim in this article is to supplement the set of strong properties introduced in the preceding article ([Dix94]) with a set of weak principles in order to characterize semantics of logic programs. In [Dix94] we introduced our point of view: we observed that all semantics induce in a natural way a sceptical non-monotonic entailment relation SEM scept . We ask for the properties of these sceptical relations and use them to describe all possible semantics. We collect in this paper serious shortcomings of some semantics proposed recently. Their strange behaviour led us to formulate in a natural way certain principles to avoid these problems. We argue that any well-behaved semantics should satisfy these principles. The main results state that our list of weak principles is complete in the following sense: any well-behaved-semantics is an extension of the well-founded semantics WFS and coincides for stratified programs with Apt, Blair, and Walker's supported model M supp P . We also claim that two extensions of the well-founded semantics (introduced in the preceding article) are uniquely characterized by their strong and weak properties.

Recently, H Herre developed a framework for nonmonotonic inference relations using closure operators I that associate with every theory Φ a subset I(Φ) of its ordinary models. He asked to what extent Herbrand's Theorem is valid for these model-based operators. In particular, he asked if it is true for ⊨min, ⊨re. and ⊨rec truth in all minimal, in all recursively enumerable, or in all recursive Herbrand models, respectively. We answer these questions negatively and discuss Herbrand's Theorem also for well-known nonmonotonic theories (the closed world assumption CWA, Reiter's default logic DL), Minker's weak generalized closed world assumption WGCWA and Clark's completion comp(P), as well as for monotonic theories, e.g. for ⊨d where d is a degree of unsolvability, and for theories where I is restricted to be a subset of the Herbrand models. In case of Clark's comp(P), Minker's WGCWA and ⊨d we get some interesting positive results. We also consider the compactness-property and its relation to Herbrand's Theorem in the more general setting of nonmonotonic relations ♭ that need not fit into Herre's framework.