Learning from situated experiences for a contextual planning guidance

Chaouche, Ahmed-Chawki; Seghrouchni, Amal El Fallah; Saïdouni, Djamel Eddine

doi:10.1007/s12652-016-0342-y

Cited by 10 publications

(3 citation statements)

References 19 publications

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“…Finally, the approach of Chaouche et al [32] combines both plan composition and learning. They present AgLOTOS, an algebraic language that offers two levels of plans: 'elementary plans' are first composed to produce intention plans, which are in turn composed to build an 'agent plan' satisfying several intentions.…”

Section: Ai In the Plan Phasementioning

confidence: 99%

Agent programming in the cognitive era

Bordini

Seghrouchni

Hindriks

et al. 2020

Auton Agent Multi-Agent Syst

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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 explore the contributions that agent-oriented programming can make to the development of future intelligent systems. We briefly review the state of the art in agent programming, focussing particularly on BDI-based agent programming languages, and discuss previous work on integrating AI techniques (including machine learning) in agent-oriented programming. We argue that the unique strengths of BDI agent languages provide an ideal framework for integrating the wide range of AI capabilities necessary for progress towards the next-generation of intelligent systems. We identify a range of possible approaches to integrating AI into a BDI agent architecture. Some of these approaches, e.g., 'AI as a service', exploit immediate synergies between rapidly maturing AI techniques and agent programming, while others, e.g., 'AI embedded into agents' raise more fundamental research questions, and we sketch a programme of research directed towards identifying the most appropriate ways of integrating AI capabilities into agent programs.

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Section: Ai In the Plan Phasementioning

confidence: 99%

Agent programming in the cognitive era

Bordini

Seghrouchni

Hindriks

et al. 2020

Auton Agent Multi-Agent Syst

Self Cite

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“…Chaouche et. all [6] proposed a model to deal with multiple intentions for an agent A i . It represents an agent planP(A i ) as a tree structure composed of a set of intention plans (P j ) and elementary plans (P l,k ).P(A i ) is composed of multiple intention plansP j , and each intention plan corresponds to the achievement of an agent's intention.…”

Section: Contextual Planning System (Cps)mentioning

confidence: 99%

“…This paper is organized as follows: Section 2 introduces relevant aspects of planning and cooperation used along this paper, with a description of the contextual planning system (CPS [6]). Section 3 describes the Collective CPS (CCPS) by presenting its formal structure and related algorithms.…”

Section: Introductionmentioning

confidence: 99%

Fostering Agent Cooperation in AmI: A Context-Aware Mechanism for Dealing with Multiple Intentions

Casals¹,

Belbachir²,

Seghrouchni³

et al. 2018

Intelligent Distributed Computing XII

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Ambient Intelligent (AmI) environments dynamically provide contextual information to intelligent agents that interact with them. In such environments, could these agents cooperate to improve their goal achievement, considering multiple intentions from several agents? With multiple agents, cooperation will depend on each agent's own intentions. Agents adapt to dynamic changes in the environment using context-aware planning mechanisms such as the Contextual Planning System (CPS), which proposes an optimal plan for a single agent based on the current context. In this paper we present the Collective CPS (CCPS), an opportunistic cooperative planning mechanism for multiple agents in AmI environments. CCPS allows agents to partially delegate their own plans or to collaborate with other agents' plans during their execution, while retaining individual planning capabilities. A working scenario is shown for a realistic AmI environment, such as a smart Campus.

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