106,945 research outputs found
Strategy Synthesis for Autonomous Agents Using PRISM
We present probabilistic models for autonomous agent search and retrieve missions derived from Simulink models for an Unmanned Aerial Vehicle (UAV) and show how probabilistic model checking and the probabilistic model checker PRISM can be used for optimal controller generation. We introduce a sequence of scenarios relevant to UAVs and other autonomous agents such as underwater and ground vehicles. For each scenario we demonstrate how it can be modelled using the PRISM language, give model checking statistics and present the synthesised optimal controllers. We conclude with a discussion of the limitations when using probabilistic model checking and PRISM in this context and what steps can be taken to overcome them. In addition, we consider how the controllers can be returned to the UAV and adapted for use on larger search areas
Real-time tree search with pessimistic scenarios
Autonomous agents need to make decisions in a sequential manner, under
partially observable environment, and in consideration of how other agents
behave. In critical situations, such decisions need to be made in real time for
example to avoid collisions and recover to safe conditions. We propose a
technique of tree search where a deterministic and pessimistic scenario is used
after a specified depth. Because there is no branching with the deterministic
scenario, the proposed technique allows us to take into account the events that
can occur far ahead in the future. The effectiveness of the proposed technique
is demonstrated in Pommerman, a multi-agent environment used in a NeurIPS 2018
competition, where the agents that implement the proposed technique have won
the first and third places.Comment: 14 pages, 3 figures, Published as IBM Research Report RT098
Multimodal Interactive Learning of Primitive Actions
We describe an ongoing project in learning to perform primitive actions from
demonstrations using an interactive interface. In our previous work, we have
used demonstrations captured from humans performing actions as training samples
for a neural network-based trajectory model of actions to be performed by a
computational agent in novel setups. We found that our original framework had
some limitations that we hope to overcome by incorporating communication
between the human and the computational agent, using the interaction between
them to fine-tune the model learned by the machine. We propose a framework that
uses multimodal human-computer interaction to teach action concepts to
machines, making use of both live demonstration and communication through
natural language, as two distinct teaching modalities, while requiring few
training samples.Comment: Presented at AI-HRI AAAI-FSS, 2018 (arXiv:1809.06606
Optimal Search for a Moving Target with the Option to Wait
We investigate the problem in which an agent has to find an object that moves between two locations according to a discrete Markov process (see Pollock, 1970). At every period, the agent has three options: searching left, searching right, and waiting. We assume that waiting is costless whereas searching is costly. Waiting can be useful because it could induce a more favorable probability distribution over the two locations next period. We find an essentially unique (nearly) optimal strategy, and prove that it is characterized by two thresholds (as conjectured by Weber, 1986). We show, moreover, that it can never be optimal to search the location with the lower probability of containing the object. The latter result is far from obvious and is in clear contrast with the example in Ross (1983) for the model without waiting.We also analyze the case of multiple agents. This makes the problem a more strategic one, since now the agents not only compete against time but also against each other in finding the object. We find different kinds of subgame perfect equilibria, possibly containing strategies that are not optimal in the one agent case. We compare the various equilibria in terms of cost-effectiveness.Strategy;
Situational Grounding within Multimodal Simulations
In this paper, we argue that simulation platforms enable a novel type of
embodied spatial reasoning, one facilitated by a formal model of object and
event semantics that renders the continuous quantitative search space of an
open-world, real-time environment tractable. We provide examples for how a
semantically-informed AI system can exploit the precise, numerical information
provided by a game engine to perform qualitative reasoning about objects and
events, facilitate learning novel concepts from data, and communicate with a
human to improve its models and demonstrate its understanding. We argue that
simulation environments, and game engines in particular, bring together many
different notions of "simulation" and many different technologies to provide a
highly-effective platform for developing both AI systems and tools to
experiment in both machine and human intelligence.Comment: AAAI-19 Workshop on Games and Simulations for Artificial Intelligenc
Exploiting Heterogeneous Robotic Systems in Cooperative Missions
In this paper we consider the problem of coordinating robotic systems with
different kinematics, sensing and vision capabilities to achieve certain
mission goals. An approach that makes use of a heterogeneous team of agents has
several advantages when cost, integration of capabilities, or large search
areas need to be considered. A heterogeneous team allows for the robots to
become "specialized", accomplish sub-goals more effectively, and thus increase
the overall mission efficiency. Two main scenarios are considered in this work.
In the first case study we exploit mobility to implement a power control
algorithm that increases the Signal to Interference plus Noise Ratio (SINR)
among certain members of the network. We create realistic sensing fields and
manipulation by using the geometric properties of the sensor field-of-view and
the manipulability metric, respectively. The control strategy for each agent of
the heterogeneous system is governed by an artificial physics law that
considers the different kinematics of the agents and the environment, in a
decentralized fashion. Through simulation results we show that the network is
able to stay connected at all times and covers the environment well. The second
scenario studied in this paper is the biologically-inspired coordination of
heterogeneous physical robotic systems. A team of ground rovers, designed to
emulate desert seed-harvester ants, explore an experimental area using
behaviors fine-tuned in simulation by a genetic algorithm. Our robots
coordinate with a base station and collect clusters of resources scattered
within the experimental space. We demonstrate experimentally that through
coordination with an aerial vehicle, our ant-like ground robots are able to
collect resources two times faster than without the use of heterogeneous
coordination
Knowledge-Enabled Robotic Agents for Shelf Replenishment in Cluttered Retail Environments
Autonomous robots in unstructured and dynamically changing retail
environments have to master complex perception, knowledgeprocessing, and
manipulation tasks. To enable them to act competently, we propose a framework
based on three core components: (o) a knowledge-enabled perception system,
capable of combining diverse information sources to cope with occlusions and
stacked objects with a variety of textures and shapes, (o) knowledge processing
methods produce strategies for tidying up supermarket racks, and (o) the
necessary manipulation skills in confined spaces to arrange objects in
semi-accessible rack shelves. We demonstrate our framework in an simulated
environment as well as on a real shopping rack using a PR2 robot. Typical
supermarket products are detected and rearranged in the retail rack, tidying up
what was found to be misplaced items.Comment: published in the proceedings of AAMAS 2016 as an extended abstrac
Optimal Distributed Searching in the Plane with and without Uncertainty
We consider the problem of multiple agents or robots searching for a target
in the plane. This is motivated by Search and Rescue operations (SAR) in the
high seas which in the past were often performed with several vessels, and more
recently by swarms of aerial drones and/or unmanned surface vessels.
Coordinating such a search in an effective manner is a non trivial task. In
this paper, we develop first an optimal strategy for searching with k robots
starting from a common origin and moving at unit speed. We then apply the
results from this model to more realistic scenarios such as differential search
speeds, late arrival times to the search effort and low probability of
detection under poor visibility conditions. We show that, surprisingly, the
theoretical idealized model still governs the search with certain suitable
minor adaptations
Search in the Universe of Big Networks and Data
Searching in the Internet for some object characterised by its attributes in
the form of data, such as a hotel in a certain city whose price is less than
something, is one of our most common activities when we access the Web. We
discuss this problem in a general setting, and compute the average amount of
time and the energy it takes to find an object in an infinitely large search
space. We consider the use of N search agents which act concurrently. Both the
case where the search agent knows which way it needs to go to find the object,
and the case where the search agent is perfectly ignorant and may even head
away from the object being sought. We show that under mild conditions regarding
the randomness of the search and the use of a time-out, the search agent will
always find the object despite the fact that the search space is infinite. We
obtain a formula for the average search time and the average energy expended by
N search agents acting concurrently and independently of each other. We see
that the time-out itself can be used to minimise the search time and the amount
of energy that is consumed to find an object. An approximate formula is derived
for the number of search agents that can help us guarantee that an object is
found in a given time, and we discuss how the competition between search agents
and other agents that try to hide the data object, can be used by opposing
parties to guarantee their own success.Comment: IEEE Network Magazine - Special Issue on Networking for Big Data,
July-August 201
Generalized Regressive Motion: a Visual Cue to Collision
Brains and sensory systems evolved to guide motion. Central to this task is
controlling the approach to stationary obstacles and detecting moving
organisms. Looming has been proposed as the main monocular visual cue for
detecting the approach of other animals and avoiding collisions with stationary
obstacles. Elegant neural mechanisms for looming detection have been found in
the brain of insects and vertebrates. However, looming has not been analyzed in
the context of collisions between two moving animals. We propose an alternative
strategy, Generalized Regressive Motion (GRM), which is consistent with
recently observed behavior in fruit flies. Geometric analysis proves that GRM
is a reliable cue to collision among conspecifics, whereas agent-based modeling
suggests that GRM is a better cue than looming as a means to detect approach,
prevent collisions and maintain mobility
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