Weighted Logic is a powerful tool for the specification of calculations over semirings that depend on qualitative information. Using a novel combination of Weighted Logic and Here-and-There (HT) Logic, in which this dependence is based on intuitionistic grounds, we introduce Answer Set Programming with Algebraic Constraints (ASP( $\mathcal A \mathcal C$ )), where rules may contain constraints that compare semiring values to weighted formula evaluations. Such constraints provide streamlined access to a manifold of constructs available in ASP, like aggregates, choice constraints, and arithmetic operators. They extend some of them and provide a generic framework for defining programs with algebraic computation, which can be fruitfully used e.g. for provenance semantics of datalog programs. While undecidable in general, expressive fragments of ASP( $\mathcal A \mathcal C$ ) can be exploited for effective problem solving in a rich framework.

We present a solution to real-world train scheduling problems, involving routing, scheduling, and optimization, based on Answer Set Programming (ASP). To this end, we pursue a hybrid approach that extends ASP with difference constraints to account for a fine-grained timing. More precisely, we exemplarily show how the hybrid ASP system clingo[DL] can be used to tackle demanding planning and scheduling problems. In particular, we investigate how to boost performance by combining distinct ASP solving techniques, such as approximations and heuristics, with preprocessing and encoding techniques for tackling large-scale, real-world train-scheduling instances.

Models of mortality often require constraints in order that parameters may be estimated uniquely. It is not difficult to find references in the literature to the “identifiability problem”, and papers often give arguments to justify the choice of particular constraint systems designed to deal with this problem. Many of these models are generalised linear models, and it is known that the fitted values (of mortality) in such models are identifiable, i.e., invariant with respect to the choice of constraint systems. We show that for a wide class of forecasting models, namely ARIMA $(p,\delta, q)$ models with a fitted mean and $\delta = 1$ or 2, identifiability extends to the forecast values of mortality; this extended identifiability continues to hold when some model terms are smoothed. The results are illustrated with data on UK males from the Office for National Statistics for the age-period model, the age-period-cohort model, the age-period-cohort-improvements model of the Continuous Mortality Investigation and the Lee–Carter model.

CiaoPP is an analyzer and optimizer for logic programs, part of the Ciao Prolog system. It includes PLAI, a fixpoint algorithm for the abstract interpretation of logic programs which we adapt to use tabled constraint logic programming. In this adaptation, the tabling engine drives the fixpoint computation, while the constraint solver handles the LUB of the abstract substitutions of different clauses. That simplifies the code and improves performance, since termination, dependencies, and some crucial operations (e.g., branch switching and resumption) are directly handled by the tabling engine. Determining whether the fixpoint has been reached uses semantic equivalence, which can decide that two syntactically different abstract substitutions represent the same element in the abstract domain. Therefore, the tabling analyzer can reuse answers in more cases than an analyzer using syntactical equality. This helps achieve better performance, even taking into account the additional cost associated to these checks. Our implementation is based on the TCLP framework available in Ciao Prolog and is one-third the size of the initial fixpoint implementation in CiaoPP. Its performance has been evaluated by analyzing several programs using different abstract domains.

Logic programming with tabling and constraints (TCLP, tabled constraint logic programming) has been shown to be more expressive and in some cases more efficient than LP, CLP, or LP + tabling. Previous designs of TCLP systems did not fully use entailment to determine call/answer subsumption and did not provide a simple and well-documented interface to facilitate the integration of constraint solvers in existing tabling systems. We study the role of projection and entailment in the termination, soundness, and completeness of TCLP systems and present the design and an experimental evaluation of Mod TCLP, a framework that eases the integration of additional constraint solvers. Mod TCLP views constraint solvers as clients of the tabling system, which is generic w.r.t. the solver and only requires a clear interface from the latter. We validate our design by integrating four constraint solvers: a previously existing constraint solver for difference constraints, written in C; the standard versions of Holzbaur’s and , written in Prolog; and a new constraint solver for equations over finite lattices. We evaluate the performance of our framework in several benchmarks using the aforementioned solvers. Mod TCLP is developed in Ciao Prolog, a robust, mature, next-generation Prolog system.

We present ${{{{$\mathscr{I}$}-}\textsc{dlv}}+{{$\mathscr{MS}$}}}$ , a new answer set programming (ASP) system that integrates an efficient grounder, namely ${{{$\mathscr{I}$}-}\textsc{dlv}}$ , with an automatic selector that inductively chooses a solver: depending on some inherent features of the instantiation produced by ${{{$\mathscr{I}$}-}\textsc{dlv}}$ , machine learning techniques guide the selection of the most appropriate solver. The system participated in the latest (7th) ASP competition, winning the regular track, category SP (i.e., one processor allowed).

Agri-food globalization is having a serious adverse impact on small- and medium-sized family farms in the province of Málaga (southern Spain), 43% of which have disappeared over the last 10 years. Short food supply chains (SFSCs) are emerging as a potential option for this type of farm, but as a strategy it is apparently not being implemented strongly enough over the region as a whole. The current case study sought to explore the initiatives carried out by local producers to date in implementing SFSCs throughout the province and to examine, from the standpoint of the production sector, the constraints hindering its development and the strategies currently being adopted with a view to addressing them. The analyses carried out under local producers perspective shows us that although SFSCs are interesting for family farms, in terms of prices, economic profit and social recognition, the abilities and capacities these channels require to producers, jointly with technical, flexibility and time demands, make these channels to be not that successful and attractive. Small producers interested in SFSCs must be aware of the special importance of social linkages and the need to take care of them; as well as of the need of establishing synergies and cooperation with other producers and stakeholders, in order to facilitate the tasks associated and that not every food product suit SFSCs.

Modeling the instantaneous kinematics of lower pair linkages using joint screws and the finite kinematics with Lie group concepts is well established on a solid theoretical foundation. This allows for modeling the forward kinematics of mechanisms as well the loop closure constraints of kinematic loops. Yet there is no established approach to the modeling of complex mechanisms possessing multiple kinematic loops. For such mechanisms, it is crucial to incorporate the kinematic topology within the modeling in a consistent and systematic way. To this end, in this paper a kinematic model graph is introduced that gives rise to an ordering of the joints within a mechanism and thus allows to systematically apply established kinematics formulations. It naturally gives rise to topologically independent loops and thus to loop closure constraints. Geometric constraints as well as velocity and acceleration constraints are formulated in terms of joint screws. An extension to higher order loop constraints is presented. It is briefly discussed how the topology representation can be used to amend structural mobility criteria.

The problem of robust adaptive control of a robotic manipulator subjected to uncertain dynamics and joint space constraints is addressed in this paper. Command filters are used to overcome the time derivatives of virtual control, thus reducing the need for desired trajectory differentiations. A barrier Lyapunov function is used to deal with the joint space constraints. A robust adaptive support vector regression architecture is used to reduce filtering errors, approximation errors and handle dynamic uncertainties. The stability analysis of the closed-loop system using the Lyapunov theory permits to highlight adaptation laws and to prove that all signals of the closed-loop system are bounded. Simulations show the effectiveness of the proposed control strategy.

A very desirable Datalog extension investigated by many researchers in the last 30 years consists in allowing the use of the basic SQL aggregates min, max, count and sum in recursive rules. In this paper, we propose a simple comprehensive solution that extends the declarative least-fixpoint semantics of Horn Clauses, along with the optimization techniques used in the bottom-up implementation approach adopted by many Datalog systems. We start by identifying a large class of programs of great practical interest in which the use of min or max in recursive rules does not compromise the declarative fixpoint semantics of the programs using those rules. Then, we revisit the monotonic versions of count and sum aggregates proposed by Mazuran et al. (2013b, The VLDB Journal 22, 4, 471–493) and named, respectively, mcount and msum. Since mcount, and also msum on positive numbers, are monotonic in the lattice of set-containment, they preserve the fixpoint semantics of Horn Clauses. However, in many applications of practical interest, their use can lead to inefficiencies, that can be eliminated by combining them with max, whereby mcount and msum become the standard count and sum. Therefore, the semantics and optimization techniques of Datalog are extended to recursive programs with min, max, count and sum, making possible the advanced applications of superior performance and scalability demonstrated by BigDatalog (Shkapsky et al. 2016. In SIGMOD. ACM, 1135–1149) and Datalog-MC (Yang et al. 2017. The VLDB Journal 26, 2, 229–248).

In robotics, path planning and trajectory optimization are usually performed separately to optimize the path from the given starting point to the ending point in the presence of obstacles. In this paper, path planning and trajectory optimization for robotic manipulators are solved simultaneously by a newly developed methodology called Discrete Mechanics and Optimal Control (DMOC). In DMOC, the Lagrange–d'Alembert equation is discretized directly unlike the conventional variational optimization method in which first the Euler–Lagrange equations are derived and then discretization takes place. In this newly developed method, the constraints for optimization of a desired objective function are the forced discrete Euler–Lagrange equations. In this paper, DMOC is used for simultaneous path planning and trajectory optimization for robotic manipulators in the presence of known static obstacles. Two numerical examples, applied on a DELTA parallel robot, are discussed to show the applicability of this new methodology. The optimal results obtained from DMOC are compared with the other state-of-the-art techniques. The difficulties and problems associated in using the DMOC for Parallel Kinematic Machine (PKM) are also discussed in this paper.

Artificial lighting is a significant threat to biodiversity. Although efforts to reduce lighting are crucial for species’ conservation efforts, management is challenging because light at night is integral to modern society and light use is increasing with population and economic growth. The development and evaluation of appropriate light management strategies will require positive public support, and a comprehensive understanding of public engagement with light pollution. This is the first study to examine public engagement with reducing light at night for the protection of a threatened species. A community campaign to reduce artificial light use was initiated in 2008 to protect marine turtles at a globally significant nesting beach. Semi-structured questionnaires assessed community engagement with light-glow reduction, using an existing theoretical constraints framework. Despite high levels of cognitive and affective engagement (knowledge and concern), behavioural engagement (action) with light reduction in this community was limited. Community perceptions of light reduction were dominated by ‘uncertainty and scepticism’ and ‘externalizing responsibility/blame’, implying that behavioural engagement in this community may be increased by addressing these widely-held perceptions using modified campaign materials and/or strategic legislation. Further refinement of the theoretical constraints framework would better guide future empirical and conceptual research to improve understanding of public engagement with critical environmental issues.

The socio-economic profile of small-scale broiler farmers was studied, the factors influencing profitability were analysed and constraints to broiler farming under rural conditions were identified in Bangladesh. Primary data were collected from November 2009 to February 2010 from a total of 77 broiler farmers by direct interviewing using a semi-structured questionnaire, of which 40 were located in the Mymensingh, 22 in the Sherpur and 15 in the Kishorgonj districts of Bangladesh. Data were edited and categorised into different farm sizes (100, 200, 300, 400, 500 & 600 flock sizes). Among the 77 farms, three had 100 birds, 12 had 200 birds, four had 300 birds, 17 had 400 birds, 30 had 500 birds and 11 had 600 birds each. Data for productive performance and cost and returns were used to determine benefit cost ratios (BCR) and correlated between the selected factors. The most important factor affecting profit in this study appeared to be feed conversion ratio which resulted from quality of feed and chicks and the management techniques of the farm. It was also found that the farm size, training, education, farming experience and extension contact were significant factors affecting profitability of small-scale broiler farms. Lack of quality chicks appeared as a major complaint of the farmers and this constraint ranked highest. Price instability of both chicks and live broilers was a problem for the farmers, and ranked second among the constraints. Low price of finishing broilers, high feed cost and interruption in feed supply ranked in third, fourth and fifth places respectively. Other constraints reported by the farmers included lack of technical knowledge, biosecurity, variation in feed quality, lack of access to credit, influence of middlemen, power failure and lack of technical support or extension for farming. Finally, some conclusions and recommendations are made in order to promote small-scale broiler farming in Bangladesh.