Theory of mind refers to the attribution of mental states that humans ascribe to other humans or objects (such as computer-based systems). Recently, the attribution of mental states has been investigated toward Artificial Intelligence (AI) as a basic manner to capture people's engagement toward it, and people's perception about AI social skills and AI capabilities. In line with this idea, mental state attribution can be used as an indirect measure of students' understanding of AI functioning, and in particular of the kind of interactions students may have with AI systems. Too often is the case of people using generative AI systems in ways that exceed their actual ways of functioning. In our study, children of age in the range 9-12 were involved in one-shot unplugged activities concerning data and models. The unplugged activities were not aimed at teaching the theory of Machine Learning, but rather they were designed so as to provide awareness on some basic mechanisms and help developing a correct use of tools that are becoming more and more present in everyday life. This paper introduces the activities and reports the results that were achieved.

Accountability is a central notion in both political science and software engineering, where it underpins trust, transparency, and responsibility in complex systems. Multi-agent systems (MAS) provide fertile ground for investigating accountability as a first-class computational construct. While earlier approaches in MAS research relegated accountability to an implicit property, recent extensions model it explicitly. This paper explores accountability in the well-known multi-agent JaCaMo framework as an organizational, normative, and structural mechanism. We argue that formalizing accountability in MAS provides a robust foundation for legal reasoning, compliance verification, and transparent governance of complex distributed systems, ensuring traceable responsibilities and structured feedback flows. We introduce its conceptual foundations, the JaCaMo implementation, and its implications for legal knowledge systems, showing how accountability provides a formal bridge between normative theories and executable systems.

Artificial Intelligence is undoubtedly becoming pervasive in everyday life of everyone. In this setting, developing correct AI conception since childhood is not only a need to be addressed in educational curricula, but is also a children right. Accordingly, several initiatives at national and international levels aim at promoting AI and emerging technology literacy, supported also by a proliferation in the literature of learning courses covering a variety of topics, learning objectives and targeted ages. Schools are therefore pushed to introduce innovative activities for children in their curricula. In this paper, we report the results of a case study where we tested the contribution of an AI block-based course in developing computational thinking, and human and AI minds understanding in fifth and sixth grade children.

Process alignment aims at establishing a matching between a process model run and a log trace. To improve such a matching, process alignment techniques often exploit contextual conditions to enable computations that are more informed than the simple edit distance between model runs and log traces. The paper introduces a novel approach to process alignment which relies on contextual information expressed as responsibilities . The notion of responsibility is fundamental in business and organization models, but it is often overlooked. We show the computation of optimal alignments can take advantage of responsibilities. We leverage on them in two ways. First, responsibilities may sometimes justify deviations. In these cases, we consider them as correct behaviors rather than errors. Second, responsibilities can either be met or neglected in the execution of a trace. Thus, we prefer alignments where neglected responsibilities are minimized. The paper proposes a formal framework for responsibilities in a process model, including the definition of cost functions for computing optimal alignments. We also propose a branch-and-bound algorithm for optimal alignment computation and exemplify its usage by way of two event logs from real executions.

Ethics by Design concerns the methods, algorithms and tools needed to endow autonomous agents with the capability to reason about the ethical aspects of their decisions, and the methods, tools and formalisms to guarantee that an agent's behavior remains within given moral bounds. In this context some questions arise: How and to what extent can agents understand the social reality in which they operate, and the other intelligences (AI, animals and humans) with which they co-exist? What are the ethical concerns in the emerging new forms of society, and how do we ensure the human dimension is upheld in interactions and decisions by autonomous agents?. But overall, the central question is: "Can we, and should we, build ethically-aware agents?" This paper presents initial conclusions from the thematic day of the same name held at PRIMA2017, on October 2017.

We present the JaCaMo+ framework for programming multiagent systems (MAS), where agents interact thanks to commitment-based interaction protocols. Commitment protocols are realized as artifacts that maintain a social state and notify to the participating agents those events that are relevant to the interaction. We discuss the advantages, like increased modularity and flexibility, that are brought by commitment-ruled interactions with respect to other proposals. We trace back such advantages to the possibility of relying on a standardized commitment lifecycle. We explain how to use the framework to program interacting agents by using the Netbill protocol as running example, and the Gold Miners scenario as a more complex programming example.

The paper presents a methodology to revise a Description Logic knowledge base when exceptions are detected. The approach exploits concepts and results from techniques developed for debugging Description Logic terminologies. Debugging an inconsistent terminology amounts to identifying a minimal subs et of axioms responsible for the inconsistency (i.e., an error to be removed by a knowledge engineer). Exception handling, instead, requires to revise the axioms causing an inconsistency so that a new consistent knowledge base is obtained, encompassing the detected exception about an individual x. To this aim, we make use of a nonmonotonic extension of the Description Logic 𝒜ℒ𝒞 based on the combination of a typicality operator and the well established nonmonotonic mechanism of rational closure, which allows one to deal with prototypical properties and defeasible inheritance.

We propose a methodology to revise a Description Logic knowledge base when detecting exceptions. Our approach relies on the methodology for debugging a Description Logic terminology, addressing the problem of diagnosing inconsistent ontologies by identifying a minimal subset of axioms responsible for an inconsistency. In the approach we propose, once the source of the inconsistency has been localized, the identified axioms are revised in order to obtain a consistent knowledge base including the detected exception about an individual x. To this aim, we make use of a nonmonotonic extension of the Description Logic ALC based on the combination of a typicality operator and the well established nonmonotonic mechanism of rational closure, which allows to deal with prototypical properties and defeasible inheritance.

It is well known that the problem of finding a feasible schedule for a partially ordered set of tasks can be formulated as a Disjunctive Temporal Problem (DTP). In case we want to find a schedule by taking preferences into account, there exist extensions to DTPs that augment them by associating numeric costs to the violation of individual temporal constraints, however, such extensions make the restrictive assumption that the costs associated with constraints are independent of one another. In this paper we propose a further extension, which enables the designer to specify (directional) dependencies between the preferences associated with the constraints. Such preferences are represented by exploiting Utility Difference Networks (UDNs), that define objective functions whose structure reflects conditional independencies among the nodes of the network. The paper describes the branch-and-bound algorithm at the core of the scheduling tool we have implemented for solving this new class of problems. We also present and discuss encouraging experimental results collected in two different test domains.

Robust execution of exploration mission plans has to deal with limited computational power on-board a planetary rover, and with limited rover's autonomy. Typically, these limitations prevent the rover to synthesize a new mission plan when some unexpected contingency arises. The paper shows that when such deviations refers to anomalies on the consumption of resources, robust execution can be achieved efficiently through an action reconfiguration approach instead of a replanning from scratch. Building up on an extended action model representation, the paper proposes an effective continual planner - ReCon - that, exploiting a general purpose CSP solver, is able to (i) detect violations of mission resource constraints, and (ii) find (if any) a new configuration of actions

Diagnosing the execution of a Multiagent Plan (MAP) means identifying and explaining action failures (i.e., actions that did not reach their expected effects). Current approaches to MAP diagnosis are substantially centralized, and assume that action failures are independent of each other. In this paper, the diagnosis of MAPs, executed in a dynamic and partially observable environment, is addressed in a fully distributed and asynchronous way; in addition, action failures are no longer assumed as independent of each other. The paper presents a novel methodology, named Cooperative Weak-Committed Monitoring (CWCM), enabling agents to cooperate while monitoring their own actions. Cooperation helps the agents to cope with very scarcely observable environments: what an agent cannot observe directly can be acquired from other agents. CWCM exploits nondeterministic action models to carry out two main tasks: detecting action failures and building trajectory-sets (i.e., structures representing the knowledge an agent has about the environment in the recent past). Relying on trajectory-sets, each agent is able to explain its own action failures in terms of exogenous events that have occurred during the execution of the actions themselves. To cope with dependent failures, CWCM is coupled with a diagnostic engine that distinguishes between primary and secondary action failures. An experimental analysis demonstrates that the CWCM methodology, together with the proposed diagnostic inferences, are effective in identifying and explaining action failures even in scenarios where the system observability is significantly reduced.

The paper addresses the problem of maintaining the quality of service (QoS) of an orchestration of Web services (WS), which can be affected by exogenous events (i.e., faults). The main challenge in dealing with this problem is that typically the service where a failure is detected is not the one where a fault has occurred: faults have cascade effects on the whole orchestration of services. The paper presents a novel methodology to treat the problem that is not based on Web service (re)composition, but on an adaptive re-execution of the original orchestration. Specifically, an orchestrator Manager exploits an abstract representation of the whole orchestration and a diagnostic module to localize the source of the detected failure. Then, the Manager drives the re-execution of the orchestration by deciding which service activities can be skipped, and which others must be re-executed.

A plan carried on in the real world may be affected by a number of unexpected events,plan threats, which cause significant deviations between the intended behavior of the plan executor (i.e., the agent) and the observed one. These deviations are typically considered as action failures. This paper addresses the problem of recovering from action failures caused by a specific class of plan threats:faultsin the functionalities of the agent. The problem is approached by exploiting techniques of the Model‐Based Diagnosis (MBD) for detecting failures (plan execution monitoring) and for explaining these failures in terms of faulty functionalities (agent diagnosis). The recovery process is modeled as a replanning problem aimed at fixing the faulty components identified by the agent diagnosis. However, since the diagnosis is in general ambiguous (a failure may be explained by alternative faults), the recovery has to deal with such an uncertainty. The paper advocates the adoption of a conformant planner, which guarantees that the recovery plan, if it exists, is executable no matter what the actual cause of the failure. The paper focuses on a single agent performing its own plan, however the proposed methodology takes also into account that agents are typically situated into a multiagent scenario and that commitments between agents may exist. The repair strategy is therefore conceived to overcome the causes of a failure while assuring the commitments an agent has agreed with other team members.

The paper addresses the task of monitoring and diagnosing the execution of a multi-agent plan (MAP) which involves actions concurrently executed by a team of cooperating agents. The paper describes a weak commitment strategy to deal with cases where observability is only partial and it is not sufficient for inferring the outcome of all the actions executed so far. The paper discusses the role of target actions in providing sufficient conditions for inferring the pending outcomes in a finite time window. The action outcome provides the basis for computing plan diagnosis and for singling out the goals which will not be achieved because of an action failure.