Paolo Felli

Foundations of relational artifacts verification

Artifacts are entities characterized by data of interest (constituting the state of the artifact) in a given business application, and a lifecycle, which constrains the artifacts possible evolutions. In this paper we study relational artifacts, where data are represented by a full fledged relational database, and the lifecycle is described by a temporal/ dynamic formula expressed in µ-calculus. We then consider business processes, modeled as a set of condition/action rules, in which the execution of actions (aka tasks, or atomic services) results in new artifact states. We study conformance of such processes wrt the artifact lifecycle as well as verification of temporal/dynamic properties expressed in µ-calculus. Notice that such systems are infinite-state in general, hence undecidable. However, inspired by recent literature on database dependencies developed for data exchange, we present a natural restriction that makes such systems finite-state, and the above problems decidable.

Description logic knowledge and action bases

Description logic Knowledge and Action Bases (KAB) are a mechanism for providing both a semantically rich representation of the information on the domain of interest in terms of a description logic knowledge base and actions to change such information over time, possibly introducing new objects. We resort to a variant of DL-Lite where the unique name assumption is not enforced and where equality between objects may be asserted and inferred. Actions are specified as sets of conditional effects, where conditions are based on epistemic queries over the knowledge base (TBox and ABox), and effects are expressed in terms of new ABoxes. In this setting, we address verification of temporal properties expressed in a variant of first-order µ-calculus with quantification across states. Notably, we show decidability of verification, under a suitable restriction inspired by the notion of weak acyclicity in data exchange.

Verification of description logic knowledge and action bases

We introduce description logic (DL) Knowledge and Action Bases (KAB), a mechanism that provides both a semantically rich representation of the information on the domain of interest in terms of a DL KB and a set of actions to change such information over time, possibly introducing new objects. We resort to a variant of DL-Lite where UNA is not enforced and where equality between objects may be asserted and inferred. Actions are specified as sets of conditional effects, where conditions are based on epistemic queries over the KB (TBox and ABox), and effects are expressed in terms of new ABoxes. We address the verification of temporal properties expressed in a variant of first-order μ-calculus where a controlled form of quantification across states is allowed. Notably, we show decidability of verification, under a suitable restriction inspired by the notion of weak acyclicity in data exchange.

Goal-Based Composition of Stateful Services for Smart Homes

The emerging trend in process management and in service oriented applications is to enable the composition of new distributed processes on the basis of user requests, through (parts of) available (and often embedded in the environment) services to be composed and orchestrated in order to satisfy such requests. Here, we consider a user process as specified in terms of repeated goals that the user may choose to get fulfilled, organized in a kind of routine. Available services are suitably composed and orchestrated in order to realize such a process. In particular we focus on smart homes, in which available services are those ones offered by sensor and actuator devices deployed in the home, and the target user process is directly and continuously controlled by the inhabitants, through actual goal choices. We provide a solver that synthesizes the orchestrator for the requested process and we show its practical applicability in a real smart home use case.

Artificial Social Reasoning: Computational Mechanisms for Reasoning about Others

With a view to supporting expressive, but tractable, collaborative interactions between humans and agents, we propose an approach for representing heterogeneous agent models, i.e., with potentially diverse mental abilities and holding stereotypical characteristics as members of a social reference group. We build a computationally grounded mechanism for progressing their beliefs about others’ beliefs, supporting stereotypical as well as empathic reasoning. We comment on how this approach can be used to build finite-state games, restricting the analysis of possibly large-scale problems by focusing only on the set of plausible evolutions.

Towards team formation via automated planning

Cooperative problem solving involves four key phases: (1) finding potential members to form a team, (2) forming the team, (3) formulating a plan for the team, and (4) executing the plan. We extend recent work on multi-agent epistemic planning and apply it to the problem of team formation in a blocksworld scenario. We provide an encoding of the first three phases of team formation from the perspective of an initiator, and show how automated planning efficiently yields conditional plans that guarantee certain collective intentions will be achieved. The expressiveness of the epistemic planning formalism, which supports modelling with the nested beliefs of agents, opens the prospect of broad applicability to the operationalisation of collective intention.

Process plan controllers for non-deterministic manufacturing systems

Determining the most appropriate means of producing a given product, i.e., which manufacturing and assembly tasks need to be performed in which order and how, is termed process planning. In process planning, abstract manufacturing tasks in a process recipe are matched to available manufacturing resources, e.g., CNC machines and robots, to give an executable process plan. A process plan controller then delegates each operation in the plan to specific manufacturing resources. In this paper we present an approach to the automated computation of process plans and process plan controllers. We extend previous work to support both nondeterministic (i.e., partially controllable) resources, and to allow operations to be performed in parallel on the same part. We show how implicit fairness assumptions can be captured in this setting, and how this impacts the definition of process plans.

Supervisory Control for Behavior Composition

We relate behavior composition, a synthesis task studied in AI, to supervisory control theory from the discrete event systems field. In particular, we show that realizing (i.e., implementing) a target behavior (e.g., a house surveillance system) by suitably coordinating a collection of available behaviors (e.g., doors, lights, cameras, etc.) amounts to imposing a supervisor onto a special discrete event system. Such a link allows us to leverage on the solid foundations and extensive work on discrete event systems, including borrowing tools and ideas from it.