Numerical Abstract Persuasion Argumentation for Expressing Concurrent Multi-Agent Negotiations

Arisaka, Ryuta; Ito, Takayuki

doi:10.1007/978-3-030-56150-5_7

Cited by 4 publications

(8 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For future work, it is possible to make the situation more complex with concurrency in multiagent argumentation (Arisaka and Satoh 2018;Arisaka and Ito 2019). Complication is unbounded.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, expected acceptance statuses of both a 1 and a 5 are known with no dependency on other arguments. 3 It follows trivially from the associated nuance tuples that both a 1 and a 5 satisfy must-acceptance and not-may-rejection condition. From Fig.…”

Section: Example 1 (Labelling)mentioning

confidence: 98%

“…Moreover, with studies of argumentation expanding into multi-agent systems, for argumentation-based negotiations (Cf. two surveys [18,37] for two-party negotiations and a recent work on multi-party current negotiations [3]), strategic dialogue games and persuasions [2, 6, 24-28, 31, 35, 36, 38-41], and others, it is preferable that an argumentation theory be able to accommodate a different nuance of arguments' acceptability locally per argument, and yet somehow in a logically principled manner. Future applications into the domain in mind, may-must scales are given to each argument, like local constraints in ADF, ensuring the locality.…”

Section: Motivation For May-must Scalesmentioning

confidence: 99%

See 2 more Smart Citations

Broadening Label-Based Argumentation Semantics with May-Must Scales

Arisaka

Ito

2020

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

The semantics as to which set of arguments in a given argumentation graph may be acceptable (acceptability semantics) can be characterised in a few different ways. Among them, the labelling-based approach allows for a concise and flexible determination of acceptability statuses of arguments through assignment of a label indicating acceptance, rejection, or undecided to each argument. In this work, we contemplate a way of broadening it by accommodating mayand must-conditions for an argument to be accepted and rejected, as determined by the number(s) of rejected and accepted attacking arguments. We show that the broadened label-based semantics can be used to express more mild indeterminacy than inconsistency for acceptability judgement when, for example, it may be the case that an argument is accepted and when it may also be the case that it is rejected. We identify that finding which conditions a labelling satisfies for every argument can be an undecidable problem, which has an unfavourable implication to semantics. We propose to address this problem by enforcing a labelling to maximally respect the conditions, while keeping the rest that would necessarily cause non-termination labelled undecided.

show abstract

“…For future work, it is possible to make the situation more complex with concurrency in multiagent argumentation (Arisaka and Satoh 2018;Arisaka and Ito 2019). Complication is unbounded.…”

Section: Discussionmentioning

confidence: 99%

Section: Example 1 (Labelling)mentioning

confidence: 98%

Section: Motivation For May-must Scalesmentioning

confidence: 99%

See 1 more Smart Citation

Broadening Label-Based Argumentation Semantics with May-Must Scales

Arisaka

Ito

2020

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…Two other formalisms seem able to model the above causality more closely. First, the dynamic relations as found in particular in [1,3] can express causal relation. Specifically, the theory allows generation (as well as elimination and alteration) of an argument from acceptable arguments.…”

Section: Related Workmentioning

confidence: 99%

“…With these, A 1 ⊆ A is said to be: -admissible iff A 1 is conflict-free and A 1 defends every a ∈ A 1 . 1 We denote the set of all admissible sets of F D by D(ad, F D ). -complete iff A 1 ∈ D(ad, F D ) and every a ∈ A A 1 defends is in A 1 .…”

Section: Technical Preliminariesmentioning

confidence: 99%

Let’s Accept a Mission Impossible with Formal Argumentation, or Not

Arisaka

Ito

2020

Advances in Intelligent Systems and Computing

Self Cite

View full text Add to dashboard Cite

We show with a plain example how causal dependency among arguments could lead to an infeasible acceptability judgement in abstract argumentation resulting in acceptance of some argument when no arguments that could engender it are acceptable. While the dependency is somewhat like necessary support relation in the literature, differences are still clearly noticeable, as we are to show. We present abstract argumentation theory with a causal relation, which can be termed causal abstract argumentation or as bipolar argumentation with causal support interpretation. We formulate causal acceptability semantics for this theory as the counterparts of acceptability semantics in abstract argumentation, also detailing the relation between them.

show abstract

A Systematic Mapping Study on Automated Negotiation for Autonomous Intelligent Agents

Memon,

Scoccia,

Autili

2023

Preprint

View full text Add to dashboard Cite

Autonomous intelligent agents are known as artificial intelligence software entities that can act on their own and can take decisions without any human intervention. The communication between such systems to reach an agreement for problem-solving is known as automated negotiation. This study aims to systematically identify and analyze the literature on automated negotiation. For this purpose, we performed a systematic mapping study (SMS) starting from 73,760 potentially relevant studies. Through a precise selection procedure, we identified 53 primary studies, published from the year 2000 onward, which were analyzed by applying a rigorous classification framework. As a result, we provide an up-to-date map of the state-of-the-art research in the automated negotiation domain. We also provide a replication package to help researchers replicate and verify our systematic mapping study. The results and findings will benefit researchers and practitioners to identify the research gap and conduct further research to bring dedicated solutions for automated negotiation.

show abstract

Numerical Abstract Persuasion Argumentation for Expressing Concurrent Multi-Agent Negotiations

Cited by 4 publications

References 10 publications

Broadening Label-Based Argumentation Semantics with May-Must Scales

Broadening Label-Based Argumentation Semantics with May-Must Scales

Let’s Accept a Mission Impossible with Formal Argumentation, or Not

A Systematic Mapping Study on Automated Negotiation for Autonomous Intelligent Agents

Contact Info

Product

Resources

About