The objective of my research project is the formal treatment of memory issues in Intelligent Software Agents. I extend recent work which proposed a (partial) formalization of SOAR architecture in modal logic, reasoning on a particular type of agents: resource-bounded agents. I introduce explicit treatment of time instants and time intervals by means of Metric Temporal Logic, both in the background logic and in mental operations.

We consider a market setting in which buyers are individuals of a population, whose relationships are represented by an underlying social graph. Given buyers valuations for the items being sold, an outcome consists of a pricing of the objects and an allocation of bundles to the buyers. An outcome is social envy-free if no buyer strictly prefers the bundles of her neighbors in the social graph. We focus on the revenue maximization problem in multi-unit markets, in which there are multiple copies of a same item being sold and each buyer is assigned a set of identical items. We consider the four different cases arising by considering different buyers valuations, i.e., single-minded or general, and by adopting different forms of pricing, that is item- or bundle-pricing. For all the above cases we show the hardness of the revenue maximization problem and give corresponding approximation results. All our approximation bounds are optimal or nearly optimal. Moreover, we provide an optimal allocation algorithm for general valuations with item-pricing, under the assumption of social graphs of bounded treewidth. Finally, we determine optimal bounds on the corresponding price of envy-freeness, that is on the worst case ratio between the maximum revenue that can be achieved without envy-freeness constraints, and the one obtainable in case of social relationships. Some of our results close hardness open questions or improve already known ones in the literature concerning the classical setting without sociality.

In this paper we focus on Social Distance Games (SDGs), Coalition formation is a pervasive aspect of social life and an important subclass of HGs introduced in (Brânzei and it has been studied extensively in algorithmic game theory Larson 2011) where agent utilities are based on the concept using the natural model of Hedonic Games (HGs), introduced of social distance (i.e., the number of hops required to reach in (Dreze and Greenberg 1980) and further explored one node from another), which has become famous since in (Aziz, Brandt, and Harrenstein 2011; Aziz, Brandt, and Milgram's study on six degrees of separation. In SDGs the Seedig 2013; Banerjee, Konishi, and Sönmez 2001; Bogomolnaia utility of an agent is given by the average inverse distance and Jackson 2002; Elkind and Wooldridge 2009; from all the other nodes in her coalition, that is by her harmonic Elkind, Fanelli, and Flammini 2016; Gairing and Savani centrality (Boldi and Vigna 2014) divided by the size 2010). A HG consists of a set of selfish agents (humans, of the coalition. The basic idea is that the agents prefer to robots, software agents, etc.) having preferences over coalitions maintain ties with other agents who are close to them. The that might include them, regardless of which other utility formulation is a variant of the closeness centrality and coalitions may or may not be present. The outcome is a partition reflects the principle of homophily, that similarity breeds of the agent set into disjoint coalitions (or clusters), connection and people tend to form communities with similar referred to as a clustering or coalition structure.

Developing the capabilities of children to comprehend written texts is key to their development as young adults. Text comprehension skills develop enormously from the age of 7- 8 until the age of 11. Nowadays, several young children (˜5% – 10% of novice readers) turn out to be poor (text) comprehenders: they demonstrate text comprehension difficulties, related to inference-making skills, despite proficiency in lowlevel cognitive skills like word decoding. Though there are several pencil-and-paper reading interventions for improving inference-making skills on text, and addressed to poor comprehenders, the design and evaluation of Adaptive Learning Systems (ALSs) are lagging behind. The use of more intelligent ALSs to custom-tailor such interventions in the form of games for poor comprehenders has tremendous potential. Our system embodies that potential. This paper presents the design of our ALS by focusing on its intelligent adaptive engine and the related conceptual models, and by presenting the visual interfaces for story telling and gaming.

The PDDL+ language has been mainly devised to allow modelling of real-world systems, with continuous, time-dependant dynamics. Several interesting case studies with these characteristics have been also proposed, to test the language expressiveness and the capabilities of the support tools. However, most of these case studies have not been completely developed so far. In this paper we focus on the batch chemical plant case study, a very complex hybrid system with nonlinear dynamics that could represent a challenging benchmark problem for planning techniques and tools. We present a complete PDDL+ model for such system, and show an example application where the UPMurphi universal planner is used to generate a set of production policies for the plant.

Systems subject to (continuous) physical effects and controlled by (discrete) digital equipments, are today very common. Thus, many realistic domains where planning is required are represented by hybrid systems , i.e., systems containing both discrete and continuous values, with possibly a nonlinear continuous dynamics. The PDDL+ language allows one to model these domains, however the current tools can generally handle only planning problems on (possibly hybrid) systems with linear dynamics. Therefore, universal planning applied to hybrid systems and, in general, to non-linear systems is completely out of scope for such tools. In this paper, we propose the use of explicit model checking-based techniques to solve universal planning problems on such hardly-approachable domains.