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In multi-agent systems the main process responsible for obtaining information about the environment is perception, generally this process is performed passively regardless the agent&#39;s intentional state. However, especially when inserted in the real world, a frequent problem is that agents have partial perception of the environment, failing to perceive some relevant information. To circumvent this problem, a solution is to actively take actions to perceive what is of interest to the agent, for example, in a computer vision system, the camera can be repositioned to have a better view of an object. This work aims to develop an active perception model integrated with the reasoning cycle of BDI agents. Experiments are performed using BDI agents with ROS to command unmanned aerial vehicles to analyze the benefits and impacts of using cognitive agents with active perception to program robot intelligence.

AAMAS 2021

Active Perception within BDI Agents Reasoning Cycle

AAMAS is the largest and most influential conference in the area of agents and multiagent systems, bringing together researchers and practitioners in all areas of agent technology and providing and internationally renowned high-profile forum for publishing and finding out about the latest developments in the field. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).  

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In multi-agent systems the main process responsible for obtaining information about the environment is perception, generally this process is performed passively regardless the agent's intentional state. However, especially when inserted in the real world, a frequent problem is that agents have partial perception of the environment, failing to perceive some relevant information. To circumvent this problem, a solution is to actively take actions to perceive what is of interest to the agent, for example, in a computer vision system, the camera can be repositioned to have a better view of an object. This work aims to develop an active perception model integrated with the reasoning cycle of BDI agents. Experiments are performed using BDI agents with ROS to command unmanned aerial vehicles to analyze the benefits and impacts of using cognitive agents with active perception to program robot intelligence.

technical paper

The area of computational social choice (COMSOC) analyzes collective decision problems from an algorithmic perspective. So far, the main focus in this area lied on analyzing problems where a single preference relation for each agent is given and a single solution reflecting all agents' preferences needs to be found. However, this modeling is often not rich enough to capture the changing and ambivalent nature of real-world problems. We will argue that one possibility to incorporate such aspects is to allow for multiple preference profiles in the input and multiple solutions in the output. We systematically review different types of arising settings, point out how classical problems and solution concepts can be generalized, and identify several research challenges.

Broadening the Research Agenda for Computational Social Choice: Multiple Preference Profiles and Multiple Solutions (Blue Sky Ideas Track)

Given their installation on nearly a billion consumer devices around the world, consumers clearly enjoy using voice-driven assistants such as Alexa, Siri and Google Home, and find it compelling to interact with AI agents as quasi-human entities. Inevitably, people who are accustomed to using voice-driven assistants at home and in the car will expect to use such technologies in the workplace. What form will this take? Simple extrapolations from consumer space (e.g. running meetings or presentations) promise only modest value. I propose that AAMAS and the AI research community should pursue a bolder vision in which software agents act as quasi-human collaborators on core business intelligence tasks that entail analyzing data, diagnosing problems, and making decisions. Moreover, these business intelligence agents should communicate multi-modally, i.e. they must understand and employ speech complemented by non-verbal behaviors such as pointing, eye gaze, and facial expression. I outline requisite agent, MAS and AI technologies and pose several fundamental research challenges raised by this vision.

Multi-modal Agents for Business Intelligence (Blue Sky Ideas Track)

Machine learning and artificial intelligence models that interact with and in an environment will unavoidably have impact on this environment and change it. This is often a problem as many methods do not anticipate such a change in the environment and thus may start acting sub-optimally. Although efforts are made to deal with this problem, we believe that a lot of potential is unused. Driven by the recent success of predictive machine learning, we believe that in many scenarios one can predict when and how a change in the environment will occur. In this paper we introduce a blueprint that intimately connects this idea to the multiagent setting, showing that the multiagent community has a pivotal role to play in addressing the challenging problem of changing environments.

Environment Shift Games: Are Multiple Agents the Solution, and not the Problem, to Non-Stationarity? (Blue Sky Ideas Track)

A fundamental task for artificial intelligence is learning. Deep Neural Networks have proven to cope perfectly with all learning paradigms, i.e. supervised, unsupervised, and reinforcement learning. Nevertheless, traditional deep learning approaches make use of cloud computing facilities and do not scale well to autonomous agents with low computational resources. Even in the cloud, they suffer from computational and memory limitations, and they cannot be used to model adequately large physical worlds for agents which assume networks with billions of neurons. These issues are addressed in the last few years by the emerging topic of sparse training, which trains sparse networks from scratch. This paper discusses sparse training state-of-the-art, its challenges and limitations while introducing a couple of new theoretical research directions which has the potential of alleviating sparse training limitations to push deep learning scalability well beyond its current boundaries. Nevertheless, the theoretical advancements impact in complex multi-agents settings is discussed from a real-world perspective, using the smart grid case study.

Sparse Training Theory for Scalable and Efficient Agents (Blue Sky Ideas Track)

We identify several challenges and opportunities opened to agent and multiagent systems, following the recent developments in the domain of Earth observation constellations. We focus on three challenge categories that manifest in this field: (i) configuration problems of constellations and ground stations used to operate them, potentially owned by different actors, as to provide better services and coordination; (ii) offline planning and scheduling problems, which consist in finding solution methods to schedule observation and upload/download tasks over the constellation; (iii) the design of efficient and reactive online operation methods as to adapt schedules in dynamic settings. Being naturally distributed and composed of multiple entities and users, these problems clearly fit the multiagent paradigm, and may challenge researchers for many years.

Autonomous Agents and Multiagent Systems Challenges in Earth Observation Satellite Constellations (Blue Sky Ideas Track)

This paper presents objective metrics for how explainable artificial intelligence (XAI) can be quantified. Through an overview of current trends, we show that many explanations are generated post-hoc and independent of the agent's logical process, which in turn creates explanations with limited meaning as they lack transparency and fidelity. While user studies are a known basis for evaluating XAI, studies that do not consider objective metrics for evaluating XAI may have limited meaning and may suffer from confirmation bias, particularly if they use low fidelity explanations unnecessarily. To avoid this issue, this paper suggests a paradigm shift in evaluating XAI that focuses on metrics that quantify the explanation itself and its appropriateness given the XAI goal. We suggest four such metrics based on performance differences, D, between the explanation's logic and the agent's actual performance, the number of rules, R, outputted by the explanation, the number of features, F, used to generate that explanation, and the stability, S, of the explanation. We believe that user studies that focus on these metrics in their evaluations are inherently more valid and should be integrated in future XAI research.

Better Metrics for Evaluating Explainable Artificial Intelligence (Blue Sky Ideas Track)

To develop and effectively deploy Trustworthy Autonomous Systems (TAS), we face various social, technological, legal, and ethical challenges in which different notions of responsibility can play a key role. In this work, we elaborate on these challenges, discuss research gaps, and show how the multidimensional notion of responsibility can play a role to bridge them. We argue that TAS requires operational tools to represent and reason about responsibilities of humans as well as AI agents. We review major challenges to which responsibility reasoning can contribute, highlight open research problems, and argue for the application of multiagent responsibility models in a variety of TAS domains.

Responsibility Research for Trustworthy Autonomous Systems (Blue Sky Ideas Track)

It is claimed that, in the nascent 'Cognitive Era', intelligent systems will be trained using machine learning techniques rather than programmed by software developers. A contrary point of view argues that machine learning has limitations, and, taken in isolation, cannot form the basis of autonomous systems capable of intelligent behaviour in complex environments. In this paper, we argue that the unique strengths of Belief-Desire-Intention (BDI) agent programming languages provide an ideal framework for integrating the wide range of AI capabilities necessary for progress towards the next-generation of intelligent systems.

Agent Programming in the Cognitive Era (JAAMAS Track)

A relevant application domain for agent-based software is given by smart mobile applications. In this context, the impressive progress of technologies makes it possible to explore the use of agent-oriented programming languages and frameworks based on cognitive architectures. Accordingly, here we provide a description of JaCa-Android, a framework based on the JaCaMo platform that allows for designing and programming smart mobile apps using BDI-based cognitive agents within the A&A conceptual model.

Programming Agent-based Mobile Apps: The JaCa-Android Framework (JAAMAS Track)

Reliable and secure communication between heterogeneously resourced autonomous agents controlled by competing stakeholders in a decentralized environment is a challenge. Agents require a means to find each other and communicate without reliance on a centralized party and participation in the system must be permissionless. We present the Agent Communication Network (ACN), a peer-to-peer lookup system that provides a distributed overlay to the Internet and addresses this problem. The ACN enables agents to find each other and to communicate safely. It achieves this by leveraging a distributed hash table for agent lookup, maintained by participating peers and through the use of public-key cryptography. The paper discusses the properties of the system and its guarantees as well as its integration with a novel multi-agent system. Preliminary benchmark results demonstrate the feasibility of the system, its performance, and its scalability.

Active Perception within BDI Agents Reasoning Cycle

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Active Perception within BDI Agents Reasoning Cycle

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Next from AAMAS 2021

Broadening the Research Agenda for Computational Social Choice: Multiple Preference Profiles and Multiple Solutions (Blue Sky Ideas Track)

Similar lecture

Distributional Monte Carlo Tree Search for Risk-Aware and Multi-Objective Reinforcement Learning

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads