Alessandro Panella

I see you there!: developing identity-preserving embodied interaction for museum exhibits

Museums are increasingly embracing technologies that provide highly-individualized and highly-interactive experiences to visitors. With embodied interaction experiences, increased localization accuracy supports greater nuance in interaction design, but there is usually a tradeoff between fast, accurate tracking and the ability to preserve the identity of users. Customization of experience relies on the ability to detect the identity of visitors, however. We present a method that combines fine-grained indoor tracking with robust preservation of the unique identities of multiple users. Our model merges input from an RFID reader with input from a commercial camera-based tracking system. We developed a probabilistic Bayesian model to infer at run-time the correct identification of the subjects in the cameras field of view. This method, tested in a lab and at a local museum, requires minimal modification to the exhibition space, while addressing several identity-preservation problems for which many indoor tracking systems do not have robust solutions.

Interactive POMDPs with finite-state models of other agents

We consider an autonomous agent facing a stochastic, partially observable, multiagent environment. In order to compute an optimal plan, the agent must accurately predict the actions of the other agents, since they influence the state of the environment and ultimately the agent’s utility. To do so, we propose a special case of interactive partially observable Markov decision process, in which the agent does not explicitly model the other agents’ beliefs and preferences, and instead represents them as stochastic processes implemented by probabilistic deterministic finite state controllers (PDFCs). The agent maintains a probability distribution over the PDFC models of the other agents, and updates this belief using Bayesian inference. Since the number of nodes of these PDFCs is unknown and unbounded, the agent places a Bayesian nonparametric prior distribution over the infinitely dimensional set of PDFCs. This allows the size of the learned models to adapt to the complexity of the observed behavior. Deriving the posterior distribution is in this case too complex to be amenable to analytical computation; therefore, we provide a Markov chain Monte Carlo algorithm that approximates the posterior beliefs over the other agents’ PDFCs, given a sequence of (possibly imperfect) observations about their behavior. Experimental results show that the learned models converge behaviorally to the true ones. We consider two settings, one in which the agent first learns, then interacts with other agents, and one in which learning and planning are interleaved. We show that the agent’s performance increases as a result of learning in both situations. Moreover, we analyze the dynamics that ensue when two agents are simultaneously learning about each other while interacting, showing in an example environment that coordination emerges naturally from our approach. Furthermore, we demonstrate how an agent can exploit the learned models to perform indirect inference over the state of the environment via the modeled agent’s actions.

Fast IP-Core Generation in a Partial Dynamic Reconfiguration Workflow

Reconfigurable devices, such as FPGAs, introduce into the design workflow of embedded systems a new degree of freedom: the designer can have the system autonomously modify the functionality carried out by the IP-core according to the applications changing needs while it runs. The Caronte methodology, based on the modular design approach, is a design workflow that allows the creation and the handling of partial dynamic reconfigurable architectures using Xilinx FPGAs. In order to speed up its execution, it is important to succeed in quickly generate the EDK-based systems that the flow requires for the elaboration of the correct partial reconfiguration bitstreams. To achieve this goal, an IP-core generator framework has been developed, it receives as input the VHDL description of the core functionality of a module, automatically produces as output an IP-core suitable to be inserted into an EDK system. This binding can be performed in a faster way than using EDK to re-create each time the entire architecture, exploiting the EDK system creator tool. IP-core generator can be used each time an IP-core has to be created, and not only in a dynamic reconfigurability environment. Several tests are presented to validate the proposed methodology

A design workflow for dynamically reconfigurable multi-FPGA systems

Multi-FPGA systems (MFSs) represent a promising technology for various applications, such as the implementation of supercomputers and parallel and computational intensive emulation systems. On the other hand, dynamic reconfigurability expands the possibilities of traditional FPGAs by providing them the capability of adapting their functionality while still running to cope with runtime environment changes. These two research directions are merged together in this work, that describes a methodology for designing dynamic reconfigurable MFSs. In this paper a novel MFS design flow has been described, which makes use of blocks reuse through dynamic reconfigurability to make the implementation of large systems feasible even on multi-FPGA architectures with strict physical constraints. Functional to this goal is the development of an algorithm for the extraction of the isomorphic structures of a circuit that extensively exploits the hierarchy of the design.

A partition-based first-order probabilistic logic to represent interactive beliefs

Being able to compactly represent large state spaces is crucial in solving a vast majority of practical stochastic planning problems. This requirement is even more stringent in the context of multi-agent systems, in which the world to be modeled also includes the mental state of other agents. This leads to a hierarchy of beliefs that results in a continuous, unbounded set of possible interactive states, as in the case of Interactive POMDPs. In this paper, we describe a novel representation for interactive belief hierarchies that combines first-order logic and probability. The semantics of this new formalism is based on recursively partitioning the belief space at each level of the hierarchy; in particular, the partitions of the belief simplex at one level constitute the vertices of the simplex at the next higher level. Since in general a set of probabilistic statements only partially specifies a probability distribution over the space of interest, we adopt the maximum entropy principle in order to convert it to a full specification.