Exploring Heuristic Action Selection in Agent Programming

Hindriks, Koen V.; Jonker, Catholijn M.; Pasman, W.

doi:10.1007/978-3-642-03278-3_2

Cited by 6 publications

(7 citation statements)

References 12 publications

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“…In intelligent agents, preferences have been used for the selection [14,19] and elimination [15,17,18] of actions or plans. The preference language on which our work is based is also used by Fritz and McIlraith [10,11] to integrate preferences into the agent programming language DT-Golog.…”

Section: Resultsmentioning

confidence: 99%

“…Hindriks et al [14] augment the specification of an agent program with utility values that provide heuristic guidance in the agent's selection of actions. Padgham and Singh [20] provide a mechanism for providing preference values for each plan which are evaluated dynamically at run time.…”

Section: Preferences In Intelligent Agentsmentioning

confidence: 99%

“…Preferences have also been used for the selection [14,19,20] and elimination [15,17,18] of actions or plans.…”

Section: Preferences In Intelligent Agentsmentioning

confidence: 99%

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Preference-based reasoning in BDI agent systems

Visser

Thangarajah

Harland

et al. 2015

Auton Agent Multi-Agent Syst

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An important feature of BDI agent systems is number of different ways in which an agent can achieve its goals. The choice of means to achieve the goal in made by the system at run time, depending on contextual information that is not available in advance. In this article, we explore ways that the user of an agent system can specify preferences which can be incorporated into the BDI execution process and used to guide the choices made. For example, a user of a travel system can specify a preferred airline, or a particular kind of accommodation, and the system will use this information to satisfy the goal and preferences, if possible. Preferences are specified in terms of properties of goals and resource usage, and are used to make two types of decisions: (a) select a plan when there is a choice and (b) determine the order in which subgoals of a plan should be pursued when their order is not fixed by design. We have implemented our preference framework in Jadex, and provide detailed case studies within the context of a holiday travel agent application.

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Section: Resultsmentioning

confidence: 99%

Section: Preferences In Intelligent Agentsmentioning

confidence: 99%

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Preference-based reasoning in BDI agent systems

Visser

Thangarajah

Harland

et al. 2015

Auton Agent Multi-Agent Syst

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“…Different works apply preference-based reasoning for controlling selection of plans in cognitive agents systems, but it has not been previously applied in the context of self-management as we do in this work [13,14,18,25]. The purpose of these works is to provide information to the agent in order to choose the appropriate plan for a given situation.…”

Section: Related Workmentioning

confidence: 99%

Using Models at Runtime to Adapt Self-managed Agents for the IoT

Ayala

Horcas

Amor

et al. 2016

Lecture Notes in Computer Science

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One of the most important challenges of this decade is the Internet of Things (IoT) that pursues the integration of real-world objects in the virtual world of the Internet. One property that charac-terises IoT systems is that they have to react to variable and continuous changes. This means that IoT systems need to work as self-managed systems to effectively manage context changes. The autonomy property inherent to software agents makes them a suitable choice for developing self-managed IoT systems. By embedding agents in the devices that compose the IoT is possible to realize a decentralized system with self-management capacities. However, in this scenario new problems arise. Firstly, current agent development approaches lack mechanisms to deal with the heterogeneity present in the IoT domain. Secondly, agents must simultaneously deal with potentially conflicting changes in their behaviour, concerning self-management and application goals. In order to afford these challenges we propose to use an approach based on Dynamic Software Product Lines (D-SPL) and preference-based reasoning. The D-SPL provides to the preference-based reasoning of the agent with the necessary information to adapt its behaviour at runtime making a tradeoff between the self-management of the system and the accomplishment of its application goals.

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“…This leaves room for refinement of the programs in several ways (see, e.g. [20,4,21] for approaches that take advantage of this).…”

Section: Observation 9 [Number Of Executed Adopt or Drop Actions] In mentioning

confidence: 99%

An empirical study of cognitive agent programs

Riemsdijk

Hindriks

Jonker

2012

MGS

Self Cite

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Various agent programming languages and frameworks have been developed by now, but very few systematic studies have been done as to how the elements in these languages may be and are in fact used in practice. Performing a study of these aspects contributes to the design of instruments for facilitating development of high-quality agent programs, namely programming language, programming guidelines and teaching methods, and development environment. In this paper we propose an approach for empirically studying how programmers use a programming language, in which we identify several analysis dimensions. We perform two case studies in which we analyze agent programs written in the GOAL agent programming language along the identified dimensions. The case studies concern programs for the dynamic Blocks World and for controlling bots in the first-person shooter game UNREAL TOURNAMENT 2004. We evaluate our experimental setup and discuss to what extent our findings generalize to other cognitive agent programming languages. This provides insight into more practical aspects of the development of agent programs, and forms the basis for improvement of instruments for facilitating agent development.

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Exploring Heuristic Action Selection in Agent Programming

Cited by 6 publications

References 12 publications

Preference-based reasoning in BDI agent systems

Preference-based reasoning in BDI agent systems

Using Models at Runtime to Adapt Self-managed Agents for the IoT

An empirical study of cognitive agent programs

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