Belief-Desire-Intention in RoboCup

The Belief-Desire-Intention (BDI) model of a rational agent proposed by Bratman has strongly influenced the research of intelligent agents in Multi-Agent Systems (MAS). Jennings extended Bratman’s concept of a single rational agent into MAS in the form of joint-intention and joint-responsibility. Kitano et al. initiated RoboCup Soccer Simulation as a standard problem in MAS analogous to the Blocks World problem in traditional AI. This has motivated many researchers from various areas of studies such as machine learning, planning, and intelligent agent research. The first RoboCup team to incorporate the BDI concept is ATHumboldt98 team by Burkhard et al. In this thesis we present a novel collaborative BDI architecture modeled for RoboCup 2D Soccer Simulation called the TA09 team which is based on Bratman’s rational agent, influenced by Cohen and Levesque’s commitment, and incorporating Jennings’ joint-intention. The TA09 team features observation-based coordination, layered planning, and dynamic formation positioning

Social Mental Shaping: Modelling the Impact of Sociality on Autonomous Agents' Mental States

This paper presents a framework that captures how the social nature of agents that are situated in a multi-agent environment impacts upon their individual mental states. Roles and relationships provide an abstraction upon which we develop the notion of social mental shaping. This allows us to extend the standard Belief-Desire-Intention model to account for how common social phenomena (e.g. cooperation, collaborative problem-solving and negotiation) can be integrated into a unified theoretical perspective that reflects a fully explicated model of the autonomous agent's mental state

A Fuzzy Belief-Desire-Intention Model for Agent-Based Image Analysis

Recent methods of image analysis in remote sensing lack a sufficient grade of robustness and transferability. Methods such as object-based image analysis (OBIA) achieve satisfying results on single images. However, the underlying rule sets for OBIA are usually too complex to be directly applied on a variety of image data without any adaptations or human interactions. Thus, recent research projects investigate the potential for integrating the agent-based paradigm with OBIA. Agent-based systems are highly adaptive and therefore robust, even under varying environmental conditions. In the context of image analysis, this means that even if the image data to be analyzed varies slightly (e.g., due to seasonal effects, different locations, atmospheric conditions, or even a slightly different sensor), agent-based methods allow to autonomously adapt existing analysis rules or segmentation results according to changing imaging situations. The basis for individual software agents’ behavior is a so-called believe-desire-intention (BDI) model. Basically, the BDI describes for each individual agent its goal(s), its assumed current situation, and some action rules potentially supporting each agent to achieve its goals. The chapter introduces a believe-desire-intention (BDI) model based on fuzzy rules in the context of agent-based image analysis, which extends the classic OBIA paradigm by the agent-based paradigm

Alert-BDI: BDI Model with Adaptive Alertness through Situational Awareness

In this paper, we address the problems faced by a group of agents that possess situational awareness, but lack a security mechanism, by the introduction of a adaptive risk management system. The Belief-Desire-Intention (BDI) architecture lacks a framework that would facilitate an adaptive risk management system that uses the situational awareness of the agents. We extend the BDI architecture with the concept of adaptive alertness. Agents can modify their level of alertness by monitoring the risks faced by them and by their peers. Alert-BDI enables the agents to detect and assess the risks faced by them in an efficient manner, thereby increasing operational efficiency and resistance against attacks.Comment: 14 pages, 3 figures. Submitted to ICACCI 2013, Mysore, Indi

cBDI-based Collaborative Control for a Robotic Wheelchair

In this paper we present a collaborative control architecture for a robotic wheelchair with the aim of providing "assistance as required". The architecture is based on cBDI - an extension to the Belief-Desire-Intention model to support human-machine collaboration. We present results of an evaluation of the architecture in a simulated environment and conclude that collaborative control could ensure "feeling in control" even under assistance

Intentions in tension : Is there more to intentional action than just belief and desire?

Understanding intending is crucial to the understanding of purposeful human action. In the philosophy of action beliefs and desires are usually taken to be the necessary conditions of intending. The disagreement over how intentions specifically are related to beliefs and desires, is often put in terms of whether intentions are independent mental states or not. Belief-desire accounts of intending don’t feature intentions as independent mental states, whereas belief-desire-intention accounts of intending do. The goal of most accounts of intentional action is to account for three senses of intentionality: intentional action, intention-with-which and intending. Intentional action and intention-with-which are usually taken to be explicable in terms of belief and desire. Thus the focus of this thesis is on intending. This thesis aims at providing a more comprehensible picture of the kinds of arguments that have been given for and against the reducibility of intentions. It also provides an overview to reductivist belief-desire accounts and nonreductivist belief-desire-intention accounts and a tentative classification of arguments against reduction. Finally a recent Humean reductivist belief-desire account of intending is explored more thoroughly

Robust execution of belief-desire-intention-based agent programs

Belief-Desire-Intention (BDI) agent systems are a popular approach to building intelligent agents for complex and dynamic domains. In the BDI approach, agents select plans to achieve their goals based on their beliefs. When BDI agents pursue multiple goals in parallel, the interleaving of steps in different plans to achieve goals may result in conflicts, e.g., where the execution of a step in one plan makes the execution of a step in another concurrently executing plan impossible. Conversely, plans may also interact positively with each other, e.g., where the execution of a step in one plan assists the execution of a step in other concurrently executing plans. To avoid negative interactions and exploit positive interactions, an intelligent agent should have the ability to reason about the interactions between its intended plans. We propose SAM, an approach to scheduling the progression of an agent’s intentions (intended plans) based on Monte-Carlo Tree Search and its variant Single-Player Monte-Carlo Tree Search. SAM is capable of selecting plans to achieve an agent’s goals and interleaving the execution steps in these plans in a domain-independent way. In addition, SAM also allows developers to customise how the agent’s goals should be achieved, and schedules the progression of the agent’s intentions in a way that best satisfies the requirements of a particular application. To illustrate the flexibility of SAM, we show how our approach can be configured to prioritise criteria relevant in a range of different scenarios. In each of these scenarios, we evaluate the performance of SAM and compare it with previous approaches to intention progression in both synthetic and real-world domains

From raw data to agent perceptions for simulation, verification, and monitoring

In this paper we present a practical solution to the problem of connecting “real world” data exchanged between sensors and actuators with the higher level of abstraction used in frameworks for multiagent systems. In particular, we show how to connect an industry-standard publish-subscribe communication protocol for embedded systems called MQTT with two Belief-Desire-Intention agent modelling and programming languages: Jason/AgentSpeak and Brahms. In the paper we describe the details of our Java implementation and we release all the code open source

Flexible conversation management using a BDI agent approach

We describe a BDI (Belief, Desire, Intention) goal-oriented architecture for a conversational virtual companion embodied as a child's Toy, designed to be both entertaining and capable of carrying out col- laborative tasks. We argue that the goal-oriented approach supports both structured conversational activities (e.g., story-telling, collaborative games) as well as more \free- owing" engaging dialogue with variation and some unpredictability. BDI plans encode the knowledge required for the structured engagements, with the use of multiple plans for conversa- tional goals providing variation in the interactions

From raw data to agent perceptions for simulation, verification, and monitoring

In this paper we present a practical solution to the problem of connecting “real world” data exchanged between sensors and actuators with the higher level of abstraction used in frameworks for multiagent systems. In particular, we show how to connect an industry-standard publish-subscribe communication protocol for embedded systems called MQTT with two Belief-Desire-Intention agent modelling and programming languages: Jason/AgentSpeak and Brahms. In the paper we describe the details of our Java implementation and we release all the code open source