Finding Mutual Exclusion Invariants in Temporal Planning Domains

We present a technique for automatically extracting temporal mutual exclusion invariants from PDDL2.2 planning instances. We first identify a set of invariant candidates by inspecting the domain and then check these candidates against properties that assure invariance. If these properties are violated, we show that it is sometimes possible to refine a candidate by adding additional propositions and turn it into a real invariant. Our technique builds on other approaches to invariant synthesis presented in the literature, but departs from their limited focus on instantaneous discrete actions by addressing temporal and numeric domains. To deal with time, we formulate invariance conditions that account for both the entire structure of the operators (including the conditions, rather than just the effects) and the possible interactions between operators. As a result, we construct a technique that is not only capable of identifying invariants for temporal domains, but is also able to find a broader set of invariants for non-temporal domains than the previous techniques

Planning-based Social Partners for Children with Autism

This paper describes the design and implementation of a planning-based socially intelligent agent built to help young children with Autism Spectrum Conditions acquire social communication skills. We explain how planning technology allowed us to satisfy agent’s design requirements that we identified through our consultations with children and carers and through a review of best practices for autism intervention.We discuss the design principles implemented, the engineering challenges faced and the lessons learned from building our pedagogical agent. We conclude by presenting substantial experimental results concerning the agent’s efficacy

Learner Modelled Environments

Learner modelled environments (LMEs) are digital environments that are capable of automatically detecting learner’s behaviours in relation to a specific knowledge domain, to reason about those behaviours in order to asses learner’s performance, skills, socio-emotional and cognitive needs, and to act accordingly in a pedagogically appropriate manner. Digital environments that possess such capabilities are typically referred to as Intelligent Learning Environments, or more traditionally – as Intelligent Tutoring Systems (ITSs)

Suckling behaviour of Apennine chamois: effects of pasture quality

Availability and quality of summer pasture influence body conditions of female ungulates and, in turn, the amount of maternal cares they provide to offspring. The intensity of maternal cares is strongly associated to winter survival of offspring, which is a key determinant of population dynamics of ungulates. Climate changes and the presence of competitors may affect the nutritional quality of summer forage for ungulates, reducing survival of offspring, but relevant information is very scarce in literature. I have evaluated the effects of pasture quality and competition with red deer Cervus elaphus on suckling behaviour and winter survival of Apennine chamois Rupicapra pyrenaica ornata kids in Abruzzo, Lazio and Molise National Park. Previous studies showed a high spatial/diet overlap between chamois and re-introduced red deer, with negative effects of the latter on pastures/diet quality and feeding intensity of female chamois. Through pellet group counts and behavioural observations, I compared suckling behaviour of chamois, as well as winter survival of chamois kids across three sites with different deer density and pasture quality (July-October 2013-2014, Sites A-B: deer present at high density; “poor” pasture; Site C: deer absent; “rich” pasture). My results have showed (i) a lower probability of suckling success, (ii) a lower suckling frequency and duration, (iii) a greater frequency of suckling attempts, (iv) a lower winter survival of chamois kids, in deer-present “poor” Sites than in the deer-free-rich one. These results suggested that frequency and intensity of maternal cares were the greatest in the area, where the quality of pasture and diet of female chamois during summer was the highest one. The current Climate change is expected to affect availability of nutritious, cold-adapted plant species for chamois, with resource exploitation by red deer further depleting pasture. Both these factors are expected to affect the present and future conservation status of Apennine chamois

Leveraging probabilistic reasoning in deterministic planning for large-scale autonomous Search-and-Tracking

Search-And-Tracking (SaT) is the problem of searching for a mobile target and tracking it once it is found. Since SaT platforms face many sources of uncertainty and operational constraints, progress in the field has been restricted to simple and unrealistic scenarios. In this paper, we propose a new hybrid approach to SaT that allows us to successfully address large-scale and complex SaT missions. The probabilistic structure of SaT is compiled into a deterministic planning model and Bayesian inference is directly incorporated in the planning mechanism. Thanks to this tight integration between automated planning and probabilistic reasoning, we are able to exploit the power of both approaches. Planning provides the tools to efficiently explore big search spaces, while Bayesian inference, by readily combining prior knowledge with observable data, allows the planner to make more informed and effective decisions. We offer experimental evidence of the potential of our approach

Deterministic versus Probabilistic Methods for Searching for an Evasive Target

Several advanced applications of autonomous aerial vehicles in civilian and military contexts involve a searching agent with imperfect sensors that seeks to locate a mobile target in a given region. Effectively managing uncertainty is key to solving the related search problem, which is why all methods devised so far hinge on a probabilistic formulation of the problem and solve it through branch-and-bound algorithms, Bayesian filtering or POMDP solvers. In this paper, we consider a class of hard search tasks involving a target that exhibits an intentional evasive behaviour and moves over a large geographical area, i.e., a target that is particularly difficult to track down and uncertain to locate. We show that, even for such a complex problem, it is advantageous to compile its probabilistic structure into a deterministic model and use standard deterministic solvers to find solutions. In particular, we formulate the search problem for our uncooperative target both as a deterministic automated planning task and as a constraint programming task and show that in both cases our solution outperforms POMDPs methods