Temporal Semantics for Concurrent METATEM

AbstractConcurrentMetateMis a programming language based on the notion of concurrent, communicating objects, where each object directly executes a specification given in temporal logic, and communicates with other objects using asynchronous broadcast message-passing. Thus, ConcurrentMetateMrepresents a combination of the direct execution of temporal specifications, together with a novel model of concurrent computation. In contrast to the notions of predicates as processes and stream parallelism seen in concurrent logic languages, ConcurrentMetateMrepresents a more coarse-grained approach, where an object consists of a set of logical rules and communication is achieved by the evaluation of certain types of predicate. Representing concurrent systems as groups of such objects provides a powerful tool for modelling complex reactive systems. In order to reason about the behaviour of ConcurrentMetateMsystems, we requir a suitable semantics. Being based upon executable temporal logic, objects in isolation have an intuitive semantics. However, the addition of both operational constraints upon the object's execution and global constraints provided by the asynchronous model of concurrency and communication, complicates the overall semantics of networks of objects. It is this, more complex, semantics that we address here, where temporal semantics for varieties of ConcurrentMetateMare provided

Learning relational dynamics of stochastic domains for planning

Probabilistic planners are very flexible tools that can provide good solutions for difficult tasks. However, they rely on a model of the domain, which may be costly to either hand code or automatically learn for complex tasks. We propose a new learning approach that (a) requires only a set of state transitions to learn the model; (b) can cope with uncertainty in the effects; (c) uses a relational representation to generalize over different objects; and (d) in addition to action effects, it can also learn exogenous effects that are not related to any action, e.g., moving objects, endogenous growth and natural development. The proposed learning approach combines a multi-valued variant of inductive logic programming for the generation of candidate models, with an optimization method to select the best set of planning operators to model a problem. Finally, experimental validation is provided that shows improvements over previous work.Peer ReviewedPostprint (author's final draft

Learning relational dynamics of stochastic domains for planning

Probabilistic planners are very flexible tools that can provide good solutions for difficult tasks. However, they rely on a model of the domain, which may be costly to either hand code or automatically learn for complex tasks. We propose a new learning approach that (a) requires only a set of state transitions to learn the model; (b) can cope with uncertainty in the effects; (c) uses a relational representation to generalize over different objects; and (d) in addition to action effects, it can also learn exogenous effects that are not related to any action, e.g., moving objects, endogenous growth and natural development. The proposed learning approach combines a multi-valued variant of inductive logic programming for the generation of candidate models, with an optimization method to select the best set of planning operators to model a problem. Finally, experimental validation is provided that shows improvements over previous work.Peer ReviewedPostprint (author's final draft

Operationalizing Declarative and Procedural Knowledge: A Benchmark on Logic Programming Petri Nets (LPPNs)

Modelling, specifying and reasoning about complex systems requires to process in an integrated fashion declarative and procedural aspects of the target domain. The paper reports on an experiment conducted with a propositional version of Logic Programming Petri Nets (LPPNs), a notation extending Petri Nets with logic programming constructs. Two semantics are presented: a denotational semantics that fully maps the notation to ASP via Event Calculus; and a hybrid operational semantics that process separately the causal mechanisms via Petri nets, and the constraints associated to objects and to events via Answer Set Programming (ASP). These two alternative specifications enable an empirical evaluation in terms of computational efficiency. Experimental results show that the hybrid semantics is more efficient w.r.t. sequences, whereas the two semantics follows the same behaviour w.r.t. branchings (although the denotational one performs better in absolute terms).Comment: draft version -- update

On syntactically similar logic programs and sequential decompositions

Rule-based reasoning is an essential part of human intelligence prominently formalized in artificial intelligence research via logic programs. Describing complex objects as the composition of elementary ones is a common strategy in computer science and science in general. The author has recently introduced the sequential composition of logic programs in the context of logic-based analogical reasoning and learning in logic programming. Motivated by these applications, in this paper we construct a qualitative and algebraic notion of syntactic logic program similarity from sequential decompositions of programs. We then show how similarity can be used to answer queries across different domains via a one-step reduction. In a broader sense, this paper is a further step towards an algebraic theory of logic programming.Comment: arXiv admin note: text overlap with arXiv:1809.0993

On cascade products of answer set programs

Describing complex objects by elementary ones is a common strategy in mathematics and science in general. In their seminal 1965 paper, Kenneth Krohn and John Rhodes showed that every finite deterministic automaton can be represented (or "emulated") by a cascade product of very simple automata. This led to an elegant algebraic theory of automata based on finite semigroups (Krohn-Rhodes Theory). Surprisingly, by relating logic programs and automata, we can show in this paper that the Krohn-Rhodes Theory is applicable in Answer Set Programming (ASP). More precisely, we recast the concept of a cascade product to ASP, and prove that every program can be represented by a product of very simple programs, the reset and standard programs. Roughly, this implies that the reset and standard programs are the basic building blocks of ASP with respect to the cascade product. In a broader sense, this paper is a first step towards an algebraic theory of products and networks of nonmonotonic reasoning systems based on Krohn-Rhodes Theory, aiming at important open issues in ASP and AI in general.Comment: Appears in Theory and Practice of Logic Programmin

A Database Interface for Complex Objects

We describe a formal design for a logical query language using psi-terms as data structures to interact effectively and efficiently with a relational database. The structure of psi-terms provides an adequate representation for so-called complex objects. They generalize conventional terms used in logic programming: they are typed attributed structures, ordered thanks to a subtype ordering. Unification of psi-terms is an effective means for integrating multiple inheritance and partial information into a deduction process. We define a compact database representation for psi-terms, representing part of the subtyping relation in the database as well. We describe a retrieval algorithm based on an abstract interpretation of the psi-term unification process and prove its formal correctness. This algorithm is efficient in that it incrementally retrieves only additional facts that are actually needed by a query, and never retrieves the same fact twice

Fuzzy logic controlled miniature LEGO robot for undergraduate training system

Fuzzy logic enables designers to control complex systems more effectively than traditional approaches as it provides a simple way to arrive at a definite conclusion upon ambiguous, imprecise or noisy information. In this paper, we describe the development of two miniature LEGO robots, which are the line following and the light searching mobile robots to provide a better understanding of fuzzy logic control theory and real life application for an undergraduate training system. This study is divided into two parts. In the first part, an object sorter robot is built to perform pick and place task to load different colour objects on a fuzzy logic controlled line following robot which then carries the preloaded objects to a goal by following a white line. In the second part, an intelligent fuzzy logic controlled light searching robot with the capability to navigate in a maze is developed. All of the robots are constructed by using the LEGO Mindstorms kit. Interactive C programming language is used to program fuzzy logic robots. Experimental results show that the robots has successfully track the predefined path and navigate towards light source under the influence of the fuzzy logic controller; and therefore can be used as a training system in undergraduate fuzzy logic class