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Emotions: from psychological theories to logical formalization and implementation in a BDI agent

Emotions: from psychological theories to logical formalization and implementation in a BDI agent

The emotion generation system is a network of components called proto-specialists, each one representing an emotion among six basic ones: anger, fear, distress/sadness, enjoyment/happiness, disgust and surprise. Actually each basic emotion is a family of related affective states sharing some characteristics like their antecedent events, expression, likely behavioral response and resulting physiological activity. Other emotions are either a variation inside a basic family or a blend or mixed emotion, viz. the simultaneous feeling of several basic emotions. Each proto-specialists has several kinds of sensors to monitor internal and external stimuli and detect the elicitation conditions of this emotion. Velàsquez grounds on Roseman’s (1984) ap- praisal theory to describe the cognitive elicitors (e.g. appraisals, attributions, mem- ory...), and he also envisages non-cognitive elicitors ranked according to Izard’s (1993) view: neural, sensorimotor (e.g. the facial expression) and motivational (e.g. drives, other emotions, pain regulation). The input from these sensors either increases or decreases the intensity of the emotion. Each proto-specialist manages two thresholds of arousal: an activation threshold over which the emotion becomes active, and a saturation threshold being the maximal value of arousal for this emo- tion. The values of these thresholds set up the temperament of the agent. Finally each proto-specialist has a decay function controlling the duration of the emotion. Proto-specialists also manage moods, that differ from emotions by a lower activa- tion and thus a higher duration. All proto-specialists continuously and parallely update their intensity, depending on several parameters: previous intensity, values of elicitors, and interactions (inhibitive or excitative) with other proto-specialists that are simultaneously active.
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A situated BDI agent architecture for the GAMA modelling and simulation platform

A situated BDI agent architecture for the GAMA modelling and simulation platform

The participants were given a first basic model of that situation, containing four species of agents: road, hazard, evacuation site and driver. The behavior of the driver agents is defined by a single reflex executed at each simulation step: the agent moves towards a random target (any point on the road network), and if it reaches its destination, it chooses a new random target. A weighted graph is used for the movement of drivers: they first compute the shortest path between their location and their target, then use this path to move. The weights of the edges of the graph (roads) are updated every 10 simulation steps to take into account the number of drivers on each road. The agent speed on each road is a function of the number of drivers on this road and its maximum capacity. If a driver already has a computed path, it will not recompute it even if the weights of that path change. At the initialization of the model, the roads (154), evacuation sites (7) and the hazard (1) agents are created and initialized using shapefiles; then 500 driver agents are created and randomly placed on the roads. The GAML code for the driver species is given Figure 3. In this first basic model, drivers do not perceive the hazard and do not try to reach evacuation sites; they just keep moving randomly.
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A situated BDI agent architecture for the GAMA modelling and simulation platform

A situated BDI agent architecture for the GAMA modelling and simulation platform

Singh and Padgham [17] went one step further by proposing a framework acting like a middleware to connect components such as an ABMS platform and a BDI framework (e.g. JACK [11] or Jadex [14]). They demonstrated their framework on an application coupling the Matsim platform [4] and the GORITE BDI framework [15] for a bushfire simulation; but it aims at being generic and can be extended to couple any kind of ABMS platforms and BDI frameworks, only by implementing interfaces to plug each new component to the middleware. This approach is very powerful but remains computer-scientist-oriented, as it requires high programming skills to develop bridges for each component and to implement agents behaviours without a dedicated modelling language.
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BDI logics for BDI architectures: old problems, new perspectives

BDI logics for BDI architectures: old problems, new perspectives

Notable exceptions are [ 23 , 50 ] which import ideas from HTN planning and [ 24 ] which describes their concrete implementation framework. A Hierarchical Task Networks (HTN) is made up of a hierarchy of actions (‘tasks’) that are either basic (‘primi- tive’) or high-level (‘non-primitive’) [ 25 ]. Contrasting with the classical planning approach, HTN-based plan genera- tion decomposes high-level actions step-by-step into lower- level actions. Actions fall into two categories: STRIPS-like basic actions that can be executed directly and high-level actions that cannot. An action network is a couple d ¼ ½T ; uŠ consisting of a set of actions T and a boolean formula u. It is achieved if the set of actions T are achieved and the boolean formula u imposing restrictions on the temporal occurrence of action instances and on their pre- and post- conditions of actions holds. A decomposition method ða; w; dÞ specifies that when formula w holds, high-level action a can be decomposed into action network d: a is going to be achieved once d is achieved. For example, the method for the high-level action of submitting a paper to KI Zeitschrift is conditioned by w = ‘‘the Easychair website is available’’, and when that w holds then submitting a paper can be decomposed into an action network d ¼ ½T; uŠ where T consists of the two actions of writing a paper and uploading it and constraint u expresses that the writing action has to be performed before the uploading action. The solution for an HTN planning problem P ¼ hd ; B 0 ; Di 1 https://commons.wikimedia.org/wiki/File:Bdi-agent-architecture .
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CARS – A Spatio-Temporal BDI Recommender System: Time, Space and Uncertainty

CARS – A Spatio-Temporal BDI Recommender System: Time, Space and Uncertainty

(a) (b) Figure 6: Experimental results (selfish agents: blue, social agents: red, social distrustful agents: green): (a) average time required by the agents to reach a destination, and (b) average waiting time for the agents. ered as events. They define a language for events in which spatio-temporal knowledge is defined under the form of predicates, with an example in the traffic sce- nario. Nevertheless, the proposed framework is still in a preliminary stage and presents some drawbacks, e.g., lack of a mechanism to update such spatio- temporal beliefs and desires. Schuele et al. (Schuele and Karaenke, 2010) propose a spatial model to en- able BDI agents to move autonomously and collision- free in a spatial environment. Authors assume that in a spatial context, the agents’ knowledge about their environment is uncertain. However, this problem is not handled through a qualitative approach for spa- tial reasoning. Time reasoning is not handled nei- ther. Other relevant approaches for spatial reason- ing in BDI models are discussed in (Vahidnia et al., 2015). However, none of them consider the impre- cision and vagueness that characterise spatial knowl- edge. So far, to the best of our knowledge, many ap- proaches to reason about time in the BDI agent model are proposed in the literature (among them, see (Jarvis et al., 2005; Fisher, 2005; Sierra and Sonenberg, 2005) but none of them deals with time information imprecision. Unlike the aforementioned approaches, our approach besides combining spatial and tempo- ral reasoning within the BDI model, it addresses the open challenge of spatio-temporal information vague- ness and fuzziness that strongly characterizes such a kind of knowledge.
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BDI logics for BDI architectures: old problems, new perspectives

BDI logics for BDI architectures: old problems, new perspectives

As far as we are aware, there are few contributions relating HTN concepts with BDI agents. In [ 23 ], de Silva and Padgham show through experiments that BDI systems are more suitable when facing highly dynamic environ- ments, while HTN solutions are more efficient in a static context. In [ 50 ], Sardina et al. integrate a BDI agent system with an HTN offline planner as a ‘‘lookahead’’ component and develop a BDI agent language CANPLAN. In their architecture, an intention is a program consisting of prim- itive actions and operations on these actions. The intention is considered to be successfully executed if its corre- sponding HTN network task is accomplished. Later in [ 24 ], the authors propose a notion of ‘ideal’ (precisely, minimal non-redundant maximally-abstract) plan and compute a suboptimal ‘ideal’ plan, which is non-redundant and pre- serves abstraction as much as possible, based on the hier- archical decomposition generated by HTN planning. The above approaches inherently restrict intentions to be han- dled by an underlying predefined set of decomposition methods in a static way. However, defining all possible decompositions in the beginning may be a challenge for a modeler.
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BDI agents in social simulations: a survey

BDI agents in social simulations: a survey

3.3.4 Entertainment This goal is quite similar to training but the virtual worlds can be imaginary ones. There has been work on integrating BDI agents into games in order to provide the players with a richer, more enjoyable experience. A number of works are dedicated to the integration of autonomous agents (bots) in Unreal Tournament. Small [137] uses a simple rule-based agent called Steve, that can quickly decide its action (e.g. move to healing source) based on high-level conditions (e.g. weak energy) in a dynamic environment. To be able to compete with human players, Steve mimicks their learning process through an evolutionary algorithm: its strategies are evolved (and improved) over time. However it was still unable to consistently beat human challengers. Hindriks et al. [81] wanted to find alternatives to such reactive bots. They argue that using BDI agents instead of scripts or finite-state machines (often used in real-time games) might result in more human-like and believable behaviour, thus making the game more realistic. They add that when one gets data from observing actual game players, it is easier to specify it in terms of beliefs, desires and intentions and import it into BDI agents, than to translate it into finite-state machines. They thus tried to interface the GOAL agent programming language with Unreal Tournament 2004. Such an interface raises two main issues: finding the right level of abstraction of reasoning (the BDI agent should not have to deal with low-level details, while keeping enough control), and avoiding cognitive overload to achieve real-time responsiveness (the agent should not receive too many percepts, but should have enough information, even if possibly incomplete and uncertain, to make decisions). One of their motivations was to test agent programming platforms, in order to eventually make them more effectively applicable. They indeed noticed that BDI agent technology is not scalable yet, and they observed bad performance when increasing the number of agents.
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Using parallel computing to improve the scalability of models with BDI agents

Using parallel computing to improve the scalability of models with BDI agents

The reasoning engine of the agents with the architecture is also almost similar to the one of simple BDI, except that it integrates the notion of parallelization. Indeed, in GAMA, the agents are scheduled as follow: at each simulation step, every agents are activated one per one according to a given order, which is by default, their order of creation, but that can be simply modified. Once a BDI agent is activated, it executes its complete cycle of reasoning (perceives the world, chooses and executes plans...). One of the major modifications of our new architecture is to split this cycle in sub-steps that can be either parallelized (i.e. distribution of the computation of this sub-step on the different cores of the computer) or not. Thus, each sub-step can be executed sequentially (while keeping the activation order defined by the scheduler) or in parallel (all BDI agents execute the sub-step at the same time).
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BDI agents in social simulations: a survey

BDI agents in social simulations: a survey

3.3.4 Entertainment This goal is quite similar to training but the virtual worlds can be imaginary ones. There has been work on integrating BDI agents into games in order to provide the players with a richer, more enjoyable experience. A number of works are dedicated to the integration of autonomous agents (bots) in Unreal Tournament. Small [137] uses a simple rule-based agent called Steve, that can quickly decide its action (e.g. move to healing source) based on high-level conditions (e.g. weak energy) in a dynamic environment. To be able to compete with human players, Steve mimicks their learning process through an evolutionary algorithm: its strategies are evolved (and improved) over time. However it was still unable to consistently beat human challengers. Hindriks et al. [81] wanted to find alternatives to such reactive bots. They argue that using BDI agents instead of scripts or finite-state machines (often used in real-time games) might result in more human-like and believable behaviour, thus making the game more realistic. They add that when one gets data from observing actual game players, it is easier to specify it in terms of beliefs, desires and intentions and import it into BDI agents, than to translate it into finite-state machines. They thus tried to interface the GOAL agent programming language with Unreal Tournament 2004. Such an interface raises two main issues: finding the right level of abstraction of reasoning (the BDI agent should not have to deal with low-level details, while keeping enough control), and avoiding cognitive overload to achieve real-time responsiveness (the agent should not receive too many percepts, but should have enough information, even if possibly incomplete and uncertain, to make decisions). One of their motivations was to test agent programming platforms, in order to eventually make them more effectively applicable. They indeed noticed that BDI agent technology is not scalable yet, and they observed bad performance when increasing the number of agents.
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Création de Gateway par un agent intelligent

Création de Gateway par un agent intelligent

L'avantage premier de la construction des gateways par un agent intelligent est bien évidemment le gain de temps. L'utilisation d'agents intelligents pour la création de gateways est indispensable pour les professionnels de l'information. Ces gateways seront de meilleur qualité, nécessiteront très peu de temps à leur confection, et apporteront une valeur-ajoutée sous formes d'analyses complémentaires.

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A secure protocol based on a sedentary agent for mobile agent environments

A secure protocol based on a sedentary agent for mobile agent environments

Mobile agent systems must be equipped with safe transfer mechanisms since malicious adversaries can capture the agent and analyze its data and code while it is being transmitted. The platform that runs the mobile agent is vulnerable to several attacks due to the execution of the mobile code. Those attacks include the unauthorized reading of significant platform information, resource damages (CPU, memory, etc.) and attacks called denial of service (i.e. a partial or complete halt of the services provided by the platform). At the same time, reliable agents should have access to the platform resources in order to normally execute its code. Thus, the system should provide the platform with authentication mechanisms that enable it to specify the access rights for the mobile agents. The problem [5]
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Multiple agent possibilistic logic

Multiple agent possibilistic logic

Toulouse Cedex 09, France The paper presents a ‘multiple agent’ logic where formulas are pairs of the form (a, A), made of a proposition a and a subset of agents A. The formula (a, A) is intended to mean ‘(at least) all agents in A believe that a is true’. The formal similarity of such formulas with those of possibilistic logic, where propositions are associated with certainty levels, is emphasised. However, the subsets of agents are organised in a Boolean lattice, while certainty levels belong to a totally ordered scale. The semantics of a set of ‘multiple agent’ logic formulas is expressed by a mapping which associates a subset of agents with each interpretation (intuitively, the maximal subset of agents for whom this interpretation is possibly true). Soundness and completeness results are established. Then a joint extension of the multiple agent logic and possibilistic logic is outlined. In this extended logic, propositions are then associated with both sets of agents and certainty levels. A formula then expresses that ‘all agents in set A believe that a is true at least at some level’. The semantics is then given in terms of fuzzy sets of agents that find an interpretation more or less possible. A specific feature of possibilistic logic is that the inconsistency of a knowledge base is a matter of degree. The proposed setting enables us to distinguish between the global consistency of a set of agents and their individual consistency (where both can be a matter of degree). In particular, given a set of multiple agent possibilistic formulas, one can compute the subset of agents that are individually consistent to some degree.
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Formalismes de description des modèles agent

Formalismes de description des modèles agent

Chapitre 2 Formalismes de description des modèles agent 2.1. Introduction Ce chapitre a pour but de présenter les bonnes pratiques et l’apport de la formalisa- tion dans le domaine de la modélisation de systèmes multi-agents (SMA). Pour cela, les auteurs rappellent dans un premier temps l’intérêt de modéliser des systèmes en mettant en perspective les paradigmes associés à la démarche multi-agents. Il est alors argumenté que l’utilisation des langages de modélisation graphique permettent un meilleur échange entre les partenaires intervenant dans la conception d’un SMA. Deux types de modèles graphiques reposant sur un même socle sémantique sont ensuite présentés : UML (Unified Modeling Language) et AML (Agent Modeling Language). Le premier a une vocation généraliste et permet de faire une analyse de l’ontologie et des dynamiques du système modélisé. Le second mobilise les paradigmes particuliers aux agents et permet de faire une conception plus proche du SMA qui sera produit. Après avoir discuté de l’intérêt de chacune de ces modélisations graphiques et présenté quelques extensions possibles, le chapitre aborde l’intérêt et la façon de documenter un modèle multi-agents. Pour ce faire, le protocole ODD (Overview, Design con- cepts, Details) qui guide le modélisateur dans la rédaction d’une documentation des objectifs, des constituants et des propriétés spécifiques du modèle, est présenté.
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A New BDI Architecture To Formalize Cognitive Agent Behaviors Into Simulations

A New BDI Architecture To Formalize Cognitive Agent Behaviors Into Simulations

Abstract. Nowadays, agent-based modeling is more and more used to study complex socio-ecological systems. These last years have also seen the develop- ment of several agent-based simulation platforms. These platforms allow model- ers to easily and quickly develop models with simple agents. However, socio- ecological systems need agents able to make decisions in order to represent hu- man beings and the design of such complex agents is still an open issue: even with these platforms, designing agents able to make complex reasoning is a dif- ficult task, in particular for modelers that have no programming skill. In order to answer the modeler needs concerning complex agent design, we propose a new agent architecture based on the BDI paradigm and integrated into a simulation platform (GAMA). This paradigm allows designing expressive and realistic agents, yet, it is rarely used in simulation context. A reason is that most agent architectures based on the BDI paradigm are complex to understand and to use by non-computer-scientists. Our agent architecture answers this problem by al- lowing modelers to define complex cognitive agents in a simple way. An appli- cation of our architecture on a model concerning forest fire and firefighter heli- copters is presented.
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Modeling a real-case situation of egress using  BDI agents with emotions and social skills

Modeling a real-case situation of egress using BDI agents with emotions and social skills

To make the model closer to the reality, we calibrated it using a genetic algo- rithm. Three parameters were concerned by this calibration: the fear threshold, the crowd threshold (the limit of people around a follower which makes him start to follow the crowd instead of its leader) and the maximum search time (in seconds). These parameters are used to initialize the related attributes for each agent: the value of their attribute corresponds to a random value choice around the parameter value. We run 4 replications for each parameter value set. The final parameter set is given in Table 2 . The fitness function used for the calibra- tion was computed from following indicators: the numbers of deceased people, of injured people, of safe and sound people, and the numbers of people who exited through each exit, including the windows.
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On the Role of BDI Modelling for Integrated Control and Coordinated Behaviour in Autonomous Agents

On the Role of BDI Modelling for Integrated Control and Coordinated Behaviour in Autonomous Agents

counterfactual reasoning the agent should undertake when projecting entities' models to discover possible future goal conflicts. In general, when constructing model projections at counterfactual reasoning level N, an agent will take into account any conflicts plus any actions resulting from the anticipated resolutions to these conflicts which it had previously detected at level N-1. Values of ConflictResolutionDepth which are greater than 1, then,

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Designing agent behaviour in agent-based simulation through participatory method

Designing agent behaviour in agent-based simulation through participatory method

L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignemen[r]

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Modeling a Real-Case Situation of Egress Using BDI Agents with Emotions and Social Skills

Modeling a Real-Case Situation of Egress Using BDI Agents with Emotions and Social Skills

Keywords: BDI · Egress · Simulation · Social relationships · Emotions. 1 Introduction In the domain of evacuation studies, it is almost impossible to make real-scale experiments. Indeed, the behaviour of the people involved in fire drills is different from the one they would have in real egress, as their physical integrity is not actually threatened. Besides, it is forbidden by ethics to perform experiments with humans without telling them what they participate in. This is why sim- ulations are needed to help design better security policies. But in order to be used as scientific or decision-support tools, simulations involving humans have to be realist in terms of human evacuation behaviors and therefore consider and implement many aspects of their cognition, and in particular the factors that influence their decision-making process in emergency situation.
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Les modèles agent en géographie urbaine

Les modèles agent en géographie urbaine

7.4. Modèles agent pour simuler la dynamique des systèmes de villes Dans une perspective interurbaine il s’agit de rendre compte de l’émergence des différenciations entre les villes. D’ailleurs, pour comprendre l’évolution d’une ville donnée, parvenir à identifier les mécanismes sous-jacents à sa croissance ou au contraire à son déclin, il ne suffit pas de rechercher les facteurs explicatifs dans ses caractéristiques propres en termes de spécialisation économique, image ou forme de gouvernance. Sa place relativement aux autres villes du ou des systèmes de villes auxquels elle participe est déterminante par le biais des interactions qui opèrent entre elles, traduisant des relations de complémentarités et de concurrence. Le fait de penser les villes dans leurs interdépendances, dans leurs complémentarités fonctionnelles, dans leur organisation hiérarchique au sein d’un territoire donné est ancien, et en 1841 déjà est évoqué « un système général de villes » (Renaud, cité par [ROB 82]). Un demi-siècle plus tard Reclus [REC 95] décrit l’étroite imbrication entre les configurations hiérarchiques et spatiales des villes sous une forme très proche d’un modèle formel: la plus grande ville se trouverait au centre du pays et « les villes
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On Mobile Agent Verifiable Problems

On Mobile Agent Verifiable Problems

1.2 Related work In [13℄, Fraigniaud and Pel introdu ed two natural omputability lasses, MAD and MAV , as well as their ounterparts co - MAD and co - MAV . The lass MAD , for Mobile Agent De idable, is the lass of all mobile agent de ision problems whi h an be de ided, i.e., for whi h there exists a mobile agent proto ol su h that all agents a ept in a yes instan e, while at least one agent reje ts in a no instan e. On the other hand, the lass MAV , for Mobile Agent Veriable, is the lass of all mobile agent de ision problems whi h an be veried. More pre isely, in a yes instan e, there exists a erti ate su h that if ea h agent re eives its dedi ated pie e of it, then all agents a ept, whereas in a no instan e, for every possible erti ate, at least one agent reje ts. Certi ates are for example useful in appli ations in whi h repeated veri ations of some property are required. Fraigniaud and Pel proved in [13℄ that MAD is stri tly in luded in MAV , and they exhibited a problem whi h is omplete for MAV under an appropriate notion of ora le redu tion.
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