Complex mission or task specification languages play a fundamentally important role in human/robotic interaction. In realistic scenarios such as emergency response, specifying temporal, resource and other constraints on a mission is an essential component due to the dynamic and contingent nature of the operational environments. It is also desirable that in addition to having a formal semantics, the language should be sufficiently expressive, pragmatic and abstract. The main goal of this paper is to propose a mission specification language that meets these requirements. It is based on extending both the syntax and semantics of a well-established formalism for reasoning about action and change, Temporal Action Logic (TAL), in order to represent temporal composite actions with constraints. Fixpoints are required to specify loops and recursion in the extended language. The results include a sound and complete proof theory for this extension. To ensure that the composite language constructs are adequately grounded in the pragmatic operation of robotic systems, Task Specification Trees (TSTs) and their mapping to these constructs are proposed. The expressive and pragmatic adequacy of this approach is demonstrated using an emergency response scenario.

The paper introduces a worst-case optimal tableau algorithm for reasoning with Forest Logic Programs, a decidable fragment of Open Answer Set Programming. FoLPs are a useful device for tight integration of the Description Logic and the Logic Programming worlds: reasoning with the DL SHOQ can be simulated within the fragment. The algorithm reuses a knowledge compilation technique previously introduced, but improves on previous results by decreasing the worst-case running time with one exponential level. The decrease in complexity is due to the usage in conjunction of a new redundancy and of a new caching rule.

We define a notion of bounded action theory in the situation calculus, where the theory entails that in all situations, the number of ground fluent atoms is bounded by a constant. Such theories can still have an infinite domain and an infinite set of states. We argue that such theories are fairly common in applications, either because facts do not persist indefinitely or because one eventually forgets some facts, as one learns new ones. We discuss various ways of obtaining bounded action theories. The main result of the paper is that verification of an expressive class of first-order mu-calculus temporal properties in such theories is in fact decidable.

In this paper, we propose a semantic forgetting for arbitrary logic programs(or propositional theories) under answer set semantics,called HT-forgetting. The HT-forgetting preserves strong equivalence in the sense that strongly equivalent logic programs will remain strongly equivalent after forgetting the same set of atoms. The result of an HT-forgetting is always expressible by a logic program, and in particular, the result of an HT-forgetting in a Horn program is expressible in a Horn program; and a representation theorem shows that HT-forgetting can be precisely characterized by Zhang-Zhou's four forgetting postulates under the logic of here-and-there. We also reveal underlying connections between HT-forgetting and classical forgetting, and provide complexity results for decision problems.

Temporal logics can be used in reasoning about actions for specifying constraints on domain descriptions and temporal properties to be verified. In this paper, we exploit Bounded Model Checking (BMC) techniques in the verification of Dynamic Linear Time Temporal Logic (DLTL) properties of an action theory, which is formulated in a temporal extension of Answer Set Programming (ASP). To achieve completeness, we propose an approach to BMC which exploits the Buechi automaton construction while searching for a counterexample. We provide an encoding in ASP of the temporal action domain and of Bounded Model Checking of DLTL formulas.

The advance of Internet and Sensor technology has brought about new challenges evoked by the emergence of continuous data streams. While existing data-stream management systems allow for high-throughput stream processing, they lack complex reasoning capacities. We address this shortcoming and elaborate upon an approach to knowledge-intense stream reasoning based on Answer Set Programming (ASP). The emphasis thus shifts from rapid data processing to complex reasoning. To accommodate this in ASP, we develop new techniques that allow us to formulate problem encodings dealing with emerging as well as expiring data in a seamless way. We thus propose novel language constructs and modeling techniques for specifying and reasoning with time-decaying logic programs.

We present a system capable of automatically solving combinatorial logic puzzles given in (simplified) English. It uses an ontology to represent the puzzles in ASP which is applicable to a large set of logic puzzles. To translate the English descriptions of the puzzles into this ontology, we use a lambda-calculus based approach using Probabilistic Combinatorial Categorial Grammars (PCCG) where the meanings of words are associated with parameters to be able to distinguish between multiple meanings of the same word.

Unification in Description Logics (DLs) has been proposed as an inference service that can, for example, be used to detect redundancies in ontologies. The inexpressive Description Logic EL is of particular interest in this context since, on the one hand, several large biomedical ontologies are defined using EL. On the other hand, unification in EL has recently been shown to be NP-complete, and thus of significantly lower complexity than unification in other DLs of similarly restricted expressive power. However, the unification algorithms for EL developed so far cannot deal with general concept inclusion axioms (GCIs). This paper makes a considerable step towards addressing this problem, but the GCIs our new unification algorithm can deal with still need to satisfy a certain cycle restriction.

Answer Set Programming (ASP) is a fully-declarative logic programming paradigm, which has been proposed in the area of knowledge representation and non-monotonic reasoning. Nowadays, the formal properties of ASP are well-understood, efficient ASP systems are available, and, recently, ASP has been employed in a few industrial applications. However, ASP technology is not mature for a successful exploitation in industry yet; mainly because ASP technologies are not integrated in the well-assessed development processes and platforms which are tailored for imperative/object-oriented programming languages. In this paper we present a new programming framework blending ASP with Java. The framework is based on JASP, an hybrid language that transparently supports a bilateral interaction between ASP and Java. JASP specifications are compliant with the JPA standard to perfectly fit extensively-adopted enterprise application technologies. The framework also encompasses an implementation of JASP as a plug-in for the Eclipse platform, called JDLV, which includes a compiler from JASP to Java. Moreover, we show a real-world application developed with JASP and JDLV, which highlights the effectiveness of our ASP–Java integration framework.

Possibilistic logic is a well-known logic for reasoning under uncertainty, which is based on the idea that the epistemic state of an agent can be modeled by assigning to each possible world a degree of possibility, taken from a totally ordered, but essentially qualitative scale. Recently, a generalization has been proposed that extends possibilistic logic to a meta-epistemic logic, endowing it with the capability of reasoning about epistemic states, rather than merely constraining them. In this paper, we further develop this generalized possibilistic logic (GPL). We introduce an axiomatization showing that GPL is a fragment of a graded version of the modal logic KD, and we prove soundness and completeness w.r.t. a semantics in terms of possibility distributions. Next, we reveal a close link between the well-known stable model semantics for logic programming and the notion of minimally specific models in GPL. More generally, we analyze the relationship between the equilibrium logic of Pearce and GPL, showing that GPL can essentially be seen as a generalization of equilibrium logic, although its notion of minimal specificity is slightly more demanding than the notion of minimality underlying equilibrium logic.