We propose a novel architecture for agents to be employed in Human-AI Teaming in various, even critical, domains. The architecture is based on affective computing, empathy, and Theory of Mind (ToM) and uses a description of the user profile and the operational, professional, and ethical requirements of the domain in which the agent operates. Moreover, it encompasses: a Knowledge Graph (KG), which is aimed at representing the various kinds of knowledge of the scenario at hand; a Behavior Tree (BT) enhanced with empathy-aware capabilities; a neural component, which elaborates sensor input from devices that monitor the user and input from the KG. The enhanced BT is the component that interacts with the user, making actions or providing suggestions. It also returns feedback to the KG to update it based on user experience. This two-way interaction between KG and BT is a major novelty of our work. As further contributions, we showcase an application of the proposed architecture on a driver co-pilot scenario and provide an early stage implementation of our architecture, mainly based on a Prolog implementation of the BT component.

Epistemic Logic Programs (ELPs) extend Answer Set Programming (ASP) by incorporating epistemic operators, notably the knowledge operator K. The semantics of ELPs are defined through the concept of world views, which are sets that, in turn, comprise sets of atoms. Various semantic frameworks have been proposed, many of which, including influential early approaches, are based on reduct-based definitions. These approaches generalize the ASP methodology to ELPs by selecting a candidate world view, constructing the corresponding reduct of the program based on this candidate, computing the stable models of this reduct, and subsequently verifying if the initial candidate is indeed a world view. While specialized inference engines (ELP solvers) have been developed for certain semantic approaches, there remains no consensus regarding the “correct” semantics for ELPs, and new or variant semantics continue to emerge. In response to this evolving situation, this paper introduces a novel fast prototyping methodology that enables the implementation of solvers for any reduct-based semantics. The main advantage of this approach is the ability to rapidly experiment with new semantics on small- to medium-sized programs, as opposed to the very limited small-scale experimentation seen in the literature. This facilitates a thorough preliminary evaluation prior to committing to the more resource-intensive development of dedicated solvers. As a concrete demonstration, we apply our methodology to seminal semantic frameworks already established in the literature. Nevertheless, our approach is readily adaptable to accommodate other reduct-based semantics.

Abstract Epistemic logic programs (ELPs), extend answer set programming (ASP) with epistemic operators. The semantics of such programs is provided in terms of world views, which are sets of belief sets, that is, syntactically, sets of sets of atoms. Different semantic approaches propose different characterizations of world views. Recent work has introduced semantic properties that should be met by any semantics for ELPs, like the Epistemic Splitting Property, that, if satisfied, allows to modularly compute world views in a bottom-up fashion, analogously to “traditional” ASP. We analyze the possibility of changing the perspective, shifting from a bottom-up to a top-down approach to splitting. We propose a basic top-down approach, which we prove to be equivalent to the bottom-up one. We then propose an extended approach, where our new definition: (i) is provably applicable to many of the existing semantics; (ii) operates similarly to “traditional” ASP; (iii) provably coincides under any semantics with the bottom-up notion of splitting at least on the class of Epistemically Stratified Programs (which are, intuitively, those where the use of epistemic operators is stratified); (iv) better adheres to common ASP programming methodology.

Abstract Autonomous robots can be employed in exploring unknown environments and performing many tasks, such as, e.g. detecting areas of interest, collecting target objects, etc. Deep reinforcement learning (RL) is often used to train this kind of robot. However, concerning the artificial environments aimed at testing the robot, there is a lack of available data sets and a long time is needed to create them from scratch. A good data set is in fact usually produced with high effort in terms of cost and human work to satisfy the constraints imposed by the expected results. In the first part of this paper, we focus on the specification of the properties of the solutions needed to build a data set, making the case of environment exploration. In the proposed approach, rather than using imperative programming, we explore the possibility of generating data sets using constraint programming in Prolog. In this phase, geometric predicates describe a virtual environment according to inter-space requirements. The second part of the paper is focused on testing the generated data set in an AI gym via space search techniques. We developed a Neuro-Symbolic agent built from the following: (i) A deep Q-learning component implemented in Python, able to address via RL a search problem in the virtual space; the agent has the goal to explore a generated virtual environment to seek for a target, improving its performance through a RL process. (ii) A symbolic component able to re-address the search when the Q-learning component gets stuck in a part of the virtual environment; these components stimulate the agent to move to and explore other parts of the environment. Wide experimentation has been performed, with promising results, and is reported, to demonstrate the effectiveness of the approach.

Abstract This paper describes an intelligent ecosystem that can continuously monitor patients’ health conditions, whether at home, at work or during recreational activities, by leveraging a creative blend of wearable medical devices, intelligent agents (IA) and complex event processing (CEP). With the help of a smart application, linking wearable devices and the power of IA and CEP, patients will be constantly and actively supervised during their daily activities. This can even save their lives in case they experience sudden or gradual problems. Thanks to our system, patients with chronic illnesses that are not serious but potentially unstable will no longer overburden first aid services. This is also helpful in containing the spread of COVID-19. Specifically, in this paper, we focus on automatic monitoring of vital parameters, electrocardiogram analysis and psoriasis detection. Experimental results conducted on real patients show how promising our approach is.

This issue of TPLP contains selected papers of the 39 th

Our work describes a smart-ecosystem able to mon-itor patients' health condition, even at home or at work, by ex-ploiting a creative blend of Medical Wearables, Intelligent Agents, Complex Event Processing and Image Processing. With the help of a smart application, that links together the Wearables and the power of Artificial Intelligence, patients will be continuously and actively supervised during their daily activities. This can even save their lives, in case sudden or gradual issues should occur. Using our system, patients with non-severe though potentially unstable chronic diseases will no longer overburden first aid services. This is also useful for containing the spread of COVID-19. Specifically, in this paper we focus on automated vitals monitoring, electrocardiogram (ECG) analysis, and Psoriasis detection.

Abstract Autonomous intelligent agents are employed in many applications upon which the life and welfare of living beings and vital social functions may depend. Therefore, agents should be trustworthy. A priori certification techniques (i.e. techniques applied prior to system’s deployment) can be useful, but are not sufficient for agents that evolve, and thus modify their epistemic and belief state, and for open multi-agent systems, where heterogeneous agents can join or leave the system at any stage of its operation. In this paper, we propose/refine/extend dynamic (runtime) logic-based self-checking techniques, devised in order to be able to ensure agents’ trustworthy and ethical behaviour.

In this paper we present a methodology for introducing customizable metalogic features in logic-based knowledge representation and reasoning languages. The proposed approach is based on concepts of introspection and reflection previously introduced and discussed by various authors in relevant literature. This allows a knowledge engineer to specify enhanced reasoning engines by defining properties and meta-properties of relations as expressible for instance in OWL. We employ meta-level axiom schemata based upon a naming (reification) device. We propose general principles for extending the semantics of “host” formalisms accordingly. Consequently, suitable pre-defined libraries of properties can be made available, while user-defined new schemata are also allowed. We make the specific cases of Answer Set Programming (ASP) and Datalog±, where such features may be part of software engineering toolkits for these programming paradigms. On the one hand, concerning ASP, we extend the programming principles and practice to accommodate the proposed methodology, so as to perform meta-reasoning within the plain ASP semantics. The computational complexity of the resulting framework does not change. On the other hand, we show how metalogic features can significantly enrich Datalog± with minor changes to its operational semantics (provided in terms of “chase”) and, also in this case, no additional complexity burden.

An ontology is a formal description of knowledge as a set of concepts within a domain and the relationships that hold between them. During the past few years, ontologies became ever more popular. They can be used to describe a lot of scenarios, they are also very useful in the description of real-life situations. Our work stems from a collaboration between two departments of our University, DISIM and MESVA, within a project called eHealth, that, in the first part, focuses on the creation and the querying of an ontology based on anonymous data retrieved from first aid phone services. Once obtained the first aid data and manually created the ontology classes and properties, we are able to automatically insert the data that describes the emergency call received from the first aid operator through a Java application written by us. We analyzed those data through queries, obtaining useful results that can potentially save lives, for example, aiding the operator in the choice of the rescue vehicle to send out for an emergency, depending on the situation.

Abstract In this paper we consider Epistemic Logic Programs, which extend Answer Set Programming (ASP) with “ epistemic operators” and “ epistemic negation”, and a recent approach to the semantics of such programs in terms ofWorld Views. We propose some observations on the existence and number of world views. We show how to exploit an extended ASP semantics in order to: (i) provide a characterization of world views, different from existing ones; (ii) query world views and query the whole set of world views.

In this paper we consider how to enhance flexibility and generality in Multi-Context Systems (MCS) by considering that contexts can evolve over time, that bridge-rule application can be proactive (according to a context's specific choice), and not instantaneous but requiring an execution mechanism. We introduce bridge-rule patterns to make bridge-rules parametric w.r.t. the involved contexts.

Abstract In recent work we defined resource-based answer set semantics, which is an extension to answer set semantics stemming from the study of its relationship with linear logic. In fact, the name of the new semantics comes from the fact that in the linear-logic formulation every literal (including negative ones) were considered as a resource. In this paper, we propose a query-answering procedure reminiscent of Prolog for answer set programs under this extended semantics as an extension of XSB-resolution for logic programs with negation.1We prove formal properties of the proposed procedure. Under consideration for acceptance in TPLP.

In recent work, we provided a formulation of ASP programs in terms of linear logic theories. Answer sets were characterized in terms of maximal tensor conjunctions provable from such theories. In this paper, we propose a full comparison between Answer Set Semantics and its variation obtained by int erpreting literals (including negative literals) as resources, which leads to a different interpretation of negation. We argue that this novel view can be of both theoretical and practical interest, and we propose a modified Answer Set Semantics that we call Resource-based Answer Set Semantics. An advantage is that of avoiding inconsistencies, as every program has a (possibly empty) resource-based answer set. This implies however the introduction of a different way of representing constraints. We provide a characterization of the new semantics as a variation of the answer set semantics, and also in terms of Autoepistemic Logic. The latter characterization leads to a way of computing resource-based answer set via answer set solvers.

We are sincerely grateful to have been invited by the Guest Editor of this special issue, Prof. Francesca Lisi, to write an article in honor of our mentor Gaetano Aurelio Lanzarone (for students, colleagues and friends simply ‘Elio’). We were the first (Stefania) and last (Federico) of Elio’s students, and we both entertained a particularly deep relation with him. What we want to do here is to provide a memoir of his life and career, while his smile, laughter and humor cannot be described in words, but only in the language of our hearts and memories. We believe that Elio’s attitude towards life is well described by the words that Dante Alighieri in his ‘Divina Commedia’ (Divine Comedy, Inferno [Hell], Chant XXVI) attributes to Ulysses: ‘Considerate la vostra semenza: fatti non foste a viver come bruti, ma per seguir virtute e canoscenza’ (‘Call to mind from whence ye sprang: Ye were not form’d to live the life of brutes, But virtue to pursue and knowledge high’).

Weight constraints are a powerful programming construct that has proved very useful within the Answer Set Programming paradigm. In this paper, we argue that practical Answer Set Programming might take profit from introducing some forms of nested weight constraints. We define such empowered constraints (that we call “Nested Weight Constraints”) and discuss their semantics and their complexity.

In previous work we have proposed an extension to ASP (Answer Set Programming), called RASP, standing for ASP with Resources. RASP supports declarative reasoning on production and consumption of (amounts of) resources. The approach combines answer se

In this paper, we propose an extension of Answer Set Programming (ASP) to support declarative reasoning on consumption and production of resources. We call the proposed extension RASP, standing for “Re-sourced ASP”. Resources are modeled by introducing special atoms, called amount-atoms, to which we associate quantities that represent the avail-able amount of a certain resource. The “firing ” of a RASP-rule involving amount-atoms can both consume and produce resources. A RASP-rule can be fired several times, according to its definition and to the avail-able quantities of required resources. We define the semantics for RASP programs by extending the usual answer set semantics. Different answer sets correspond to different possible allocations of available resources. We then propose an implementation based on standard ASP-solvers. The im-plementation consists of a standard translation of each RASP-rule into a set of plain ASP rules and of an inference engine that manages the firing of RASP-rules. Key words: Answer set programming, non-monotonic logic program-

Journal Article Editorial Get access Stefania Costantini, Stefania Costantini Dipartimento di Informatica, Università degli Studi di L'Aquila, Italy Search for other works by this author on: Oxford Academic Google Scholar Richard Watson Richard Watson Department of Computer Science, Texas Tech University, College of Engineering, Lubbock, Texas, USA Search for other works by this author on: Oxford Academic Google Scholar Journal of Logic and Computation, Volume 19, Issue 4, August 2009, Pages 537–538, https://doi.org/10.1093/logcom/exn037 Published: 22 July 2009

In this paper we analyze the relationship between cyclic definitions and consistency in Gelfond-Lifschitz's answer sets semantics (originally defined as ‘stable model semantics’). This paper introduces a fundamental result, which is relevant for Answer Set programming, and planning. For the first time since the definition of the stable model semantics, the class of logic programs for which a stable model exists is given a syntactic characterization. This condition may have a practical importance both for defining new algorithms for checking consistency and computing answer sets, and for improving the existing systems. The approach of this paper is to introduce a new canonical form (to which any logic program can be reduced to), to focus the attention on cyclic dependencies. The technical result is then given in terms of programs in canonical form (canonical programs), without loss of generality: the stable models of any general logic program coincide (up to the language) to those of the corresponding canonical program. The result is based on identifying the cycles contained in the program, showing that stable models of the overall program are composed of stable models of suitable sub-programs, corresponding to the cycles, and on defining the Cycle Graph. Each vertex of this graph corresponds to one cycle, and each edge corresponds to one handle, which is a literal containing an atom that, occurring in both cycles, actually determines a connection between them. In fact, the truth value of the handle in the cycle where it appears as the head of a rule, influences the truth value of the atoms of the cycle(s) where it occurs in the body. We can therefore introduce the concept of a handle path, connecting different cycles. Cycles can be even, if they consist of an even number of rules, or vice versa they can be odd. Problems for consistency, as it is well-known, originate in the odd cycles. If for every odd cycle we can find a handle path with certain properties, then the existence of stable model is guaranteed. We will show that based on this results new classes of consistent programs can be defined, and that cycles and cycle graphs can be generalized to components and component graphs.

Normal forms for logic programs under stable/answer set semantics are introduced. We argue that these forms can simplify the study of program properties, mainly consistency. The first normal form, called the kernel of the program, is useful for studying existence and number of answer sets. A kernel program is composed of the atoms which are undefined in the Well-founded semantics, which are those that directly affect the existence of answer sets. The body of rules is composed of negative literals only. Thus, the kernel form tends to be significantly more compact than other formulations. Also, it is possible to check consistency of kernel programs in terms of colorings of the Extended Dependency Graph program representation which we previously developed. The second normal form is called 3-kernel. A 3-kernel program is composed of the atoms which are undefined in the Well-founded semantics. Rules in 3-kernel programs have at most two conditions, and each rule either belongs to a cycle, or defines a connection between cycles. 3-kernel programs may have positive conditions. The 3-kernel normal form is very useful for the static analysis of program consistency, i.e. the syntactic characterization of existence of answer sets. This result can be obtained thanks to a novel graph-like representation of programs, called Cycle Graph which presented in the companion article Costantini (2004b).

We introduce the concept of reflection principle as a knowledge representation paradigm in a computational logic setting. Reflection principles are expressed as certain kinds of logic schemata intended to capture the basic properties of the domain knowledge to be modelled. Reflection is then used to instantiate these schemata to answer specific queries about the domain. This differs from other approaches to reflection mainly in the following three ways. First, it uses logical instead of procedural reflection. Second, it aims at a cognitively adequate declarative representation of various forms of knowledge and reasoning, as opposed to reflection as a means for controlling computation or deduction. Third, it facilitates the building of a complex theory by allowing a simpler theory to be enhanced by a compact metatheory, contrary to the construction of metatheories that are only conservative extensions of the basic theory. A computational logic system for embedding reflection principles, called RCL (for Reflective Computational Logic), is presented in full detail. The system is an extension of Horn clause resolution-based logic, and is devised in a way that makes important features of reflection parametric as much as possible, so that they can be tailored according to specific needs of different application domains. Declarative and procedural semantics of the logic are described and correctness and completeness of reflection as logical inference are proved. Examples of reflection principles for three difference application areas are shown. Relationship with a variety of distinct sources within the literature on relevant topics is discussed.

This paper presents a logic programming language of novel conception, called Reflective Prolog, which allows declarative metaknowledge representation and metareasoning. The language is defined by augmenting pure Prolog (Horn clauses) with capabilities of self-reference and logical reflection. Self-reference is designed as a quotation device (a carefully defined naming relation) which allows the construction of metalevel terms that refer to object-level terms and atoms. Logical reflection is designed as an unquotation mechanism (a distinguished truth predicate) which relates names to what is named, thus extending the meaning of domain predicates. The reflection mechanism is embodied in an extended resolution procedure which automatically switches the context between levels. This implicit reflection relieves the programmer from having to explicitly deal with control aspects of the inference process. The declarative semantics of a Reflective Prolog definite program P is provided in terms of the least reflective Herbrand model of P, characterized by means of a suitable mapping defined over the Herbrand interpretations of P. The extended resolution is proved sound and complete with respect to the least reflective Herbrand model. By illustrating Reflective Prolog solutions to an organic set of problems, and by discussing the main differences with respect to other approaches to logic metaprogramming, we show that the proposed language deploys, within its field of action, greater expressive and inferential power than those available till now. The interpreter of the language has been fully implemented. Because of its enhanced power, logic semantics and working interpreter, Reflective Prolog is offered as a contribution toward making the declarative approach of logic programming applicable to the development of increasingly sophisticated knowledge-based systems.