Media Lab

Behaviors ranging from delivering newspapers to waiting tables depend on remembering previous episodes to avoid incorrect repetition. Physiologically, this requires mechanisms for long-term storage and selective retrieval of episodes based on the time of occurrence, despite variable intervals and similarity of events in a familiar environment. Here, this process has been modeled based on the physiological properties of the hippocampal formation, including mechanisms for sustained activity in entorhinal cortex and theta rhythm oscillations in hippocampal subregions. The model simulates the context-sensitive firing properties of hippocampal neurons including trial-specific firing during spatial alternation and trial by trial changes in theta phase precession on a linear track. This activity is used to guide behavior, and lesions of the hippocampal network impair memory-guided behavior. The model links data at the cellular level to behavior at the systems level, describing a physiologically plausible mechanism for the brain to recall a given episode which occurred at a specific place and time.

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2007.Includes bibliographical references (p. 127-132).This thesis presents "Connectibles," an instantiation of a tangible social network, a new type of social network application rooted in physical objects and real world social behavior. This research is inspired by social signaling and object theory, which together suggest that gifts act as physical symbols and constructors of social relationships. The Connectibles system leverages these gift-giving practices, presenting users with customizable gift objects ("connectibles") that they exchange with one another. These objects form always-on communication channels between givers and receivers. As a user collects more and more of these objects, she begins to acquire a dynamic, physical representation of and interface to her social network. The community of users' interactions implicitly ...

Several recent works considered multi agents robotics in static environments (e.g.
\cite{CC}, \cite{GraphSearch1}, \cite{CoopRobot1} and others). In this work we
examine ways of operating in dynamic environments, in which changes may take place
regardless of the agents' activity. The work focuses on a dynamic variant of the
known \emph{Cooperative Cleaners} problem (described and analyzed in~\cite{CC}).
This problem assumes a grid, part of which is ``dirty'', when the ``dirty'' part is
a connected region of the grid. On this dirty region several agents move, each
having the ability to ``clean'' the place it is located in. The dynamic variant of
the problem involves a deterministic evolution of the environment, simulating a
spreading \emph{contamination}, or \emph{fire}. A cleaning protocol for the problem
is presented, as well as several analytic bounds for it. In addition, the work
contains simulative results for the proposed protocol.

In the world of living creatures, ``simple minded'' animals often cooperate to
achieve common goals with amazing performance. One can consider this idea in
the context of robotics, and suggest models for programming goal-oriented
behavior into the members of a group of simple robots lacking global
supervision. This can be done by controlling the local interactions between the
robot agents, to have them jointly carry out a given mission. As a test case we
analyze the problem of many simple robots cooperating to clean the dirty floor
of a non-convex region in $\mbox{\bf Z}^{2}$, using the dirt on the floor as
the main means of inter-robot communication.

This paper outlines two methods for proving the isoperimetric
inequality for 4-connected shapes on the integer grid $\Z^{2}$.
Pictorially, we discuss ways to place a maximal number of unit
square tiles on a chess board so that the shape they form has a
minimal number of unit square neighbors. Previous works have shown
that ``digital disks''
have a minimum of neighbors for their area. We here characterize
all shapes that are optimal and show that they are all close to
being digital spheres. In addition, we show a similar result when
the 8-connectivity metric is assumed (i.e.~connectivity through
vertices or edges, instead of edge connectivity as in the
4-connectivity metric).

The field of multi agents and multi robotics has become increasingly popular during
the last two decades.

The motivation behind multi agents based systems is that many tasks can be
much efficiently completed by using multiple simple autonomous agents (robots,
software agents, etc.) instead of a single sophisticated one.

However, when examining such systems, one may be concerned of the price-tag
attached to the decentralized nature of swarm based approaches. Meaning, while
we simplify designs and control mechanisms in order to save costs and
computation resources, how far do our systems drift from optimality~?

This work examines this issue by constructing an optimal algorithm for the
\emph{Dynamic Cooperative Cleaners} problem. The performance of the \textbf{SWEEP} algorithm
is compared to this of an optimal algorithm. The results of this comparison
show that not only that the swarm algorithm produces close results to the
optimal solution, but also as the problem gets harder, the performance of the
two converge.

In addition, insightful results concerning optimal swarms in symmetric
environments are presented.

Several recent works considered multi-a(ge)nt robotics in static environments.
In this work we examine ways of operating in dynamic environments, where
changes take place independently of the agents' activity. The work focuses on a
dynamic variant of the \emph{Cooperative Cleaners} problem, a problem that requires several simple agents to clean a connected region of ``dirty'' pixels in $\mbox{\bf Z}^{2}$. A number of simple agents move in this dirty region, each having the ability to ``clean''
the place it is located in. Their goal is to jointly clean the given dirty region. The dynamic variant of the problem involves a
deterministic expansion of dirt in the environment, simulating spreading of
\emph{contamination}, or \emph{fire}. Theoretical lower bounds for the problem are
presented, as well as various impossibility results.
A cleaning protocol for the problem is presented, and a wealth of experimental results
testing its performance in comparison to the lower bounds.
Several analytic upper bounds for the proposed protocol are also presented, accompanied with appropriate experimental results.

This work examines the \emph{Cooperative Hunters} problem, where a swarm of
unmanned air vehicles (\emph{UAVs}) is used for searching after one or more
``evading targets'', which are moving in a predefined area while trying to
avoid a detection by the swarm.

By arranging themselves into efficient geometric flight
configurations, the UAVs optimize their integrated sensing
capabilities, enabling the search of a maximal territory.

Physical A* (PHA*) and its multi-agent version MAPHA* are algorithms that find the shortest path between two points in an unknown real physical environment with one or many mobile agents [A. Felner et al. Previous work assumed a complete sharing of knowledge between agents. Here we apply this algorithm to a more restricted model of communication which we call large pheromones, where agents communicate by writing and reading data at nodes of the graph that constitutes their environment. Previous works on pheromones usually assumed that only a limited amount of data can be written at each node. The large pheromones model assumes no limitation on the size of the pheromones and thus each agent can write its entire knowledge at a node. We show that with this model of communication the behavior of a multi-agent system is almost as good as with complete knowledge sharing. Under this model we also introduce a new type of agent, a communication agent, that is responsible for spreading the knowledge among other agents by moving around the graph and copying pheromones. Experimental results show that the contribution of communication agents is rather limited as data is already spread to other agents very well with large pheromones. 4 ARIEL FELNER ET AL.

The Linear Code is a new syntax for representing glycoconjugates and their associated molecules in a simple linear fashion. Similar to the straightforward single letter nomenclature of DNA and proteins, Linear Code presents glycoconjugates in a canonic, compact and practical form while accounting for all relevant stereochemical and structural configurations. It uses a single letter code to represent each monosaccharide and includes a condensed description of the connections between monosaccharides and their modifications, allowing a simple linear representation of these compounds. The new linear syntax enables the implementation of bioinformatics tools for investigation and analysis of glyco-molecules and their biology.

Complex network and complex systems research has been proven to have great implications in practice in many scopes including Social Networks, Biology, Disease Propagation, and Information Security. One can use complex network theory to optimize resource locations and optimize actions.
Randomly constructed graphs and probabilistic arguments lead to important conclusions with a possible great social and financial influence.

Security in online social networks has recently become a major issue for network designers and operators. Being ``open'' in their nature and offering users the ability to compose and share information, such networks may involuntarily be used as an infection platform by viruses and other kinds of malicious software. This is specifically true for mobile social networks, that allow their users to download millions of applications created by various individual programmers, some of which may be malicious or flawed. In order to detect that an application is malicious, monitoring its operation in a real environment for a significant period of time is often required. As the computation and power resources of mobile devices are very limited, a single device can monitor only a limited number of potentially malicious applications locally. In this work, we propose an efficient collaborative monitoring scheme that harnesses the collective resources of many mobile devices, generating a ``vaccination''--like effect in the network. We suggest a new local information flooding algorithm called \emph{Time-to-Live Probabilistic Propagation} (TPP). The algorithm is implemented in any mobile device, periodically monitors one or more applications and reports its conclusions to a small number of other mobile devices, who then propagate this information onwards, whereas each message has a predefined ``Time-to-Live'' (TTL) counter. The algorithm is analyzed, and is shown to outperform the existing state of the art information propagation algorithms, in terms of convergence time as well as network overhead.
We then show both analytically and experimentally that implementing the proposed algorithm significantly reduces the number of infected mobile devices.
Finally, we analytically prove that the algorithm is tolerant to the presence of adversarial agents that inject false information into the system.