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020			_a9783031946868
020			_a9783031946875 _q(electronic bk.)
020			_a3031946871 _q(electronic bk.)
024	7		_a10.1007/978-3-031-94687-5 _2doi
035			_a(OCoLC)1529919780 _z(OCoLC)1528483003 _z(OCoLC)1528958581
035		9	_a(OCLCCM-CC)1529919780
040			_aGW5XE _beng _erda _epn _cGW5XE _dEBLCP _dOCLKB _dOCLCO
041			_aeng
049			_aMAIN
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072		7	_aUYQ _2bicssc
072		7	_aCOM004000 _2bisacsh
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100	1		_aChen, Chung-Chi, _eauthor.
245	1	0	_aAgent AI for finance : _bfrom financial argument mining to agent-based modeling / _cChung-Chi Chen, Hiroya Takamura.
264		1	_aCham : _bSpringer, _c2025.
300			_a1 online resource (xii, 83 pages) : _billustrations (some color).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aSpringerBriefs in Intelligent Systems, Artificial intelligence, multiagent systems, and cognitive robotics, _x2196-5498
504			_aIncludes bibliographical references.
505	0		_aPreface -- 1. Introduction -- 2. Financial Argument Mining -- 3. Single-Agent/Model Design -- 4. Multi-Agent Interaction -- 5. Multi-Scale Model Synergy -- 6. Generative AI Application Scenarios -- 7. Looking to the Future.
520			_aThis open access book provides an overview of the current state of financial argument mining and financial text generation, and presents the authors' thoughts on the blueprint for NLP in finance in the agent AI era. Financial documents contain numerous causal inferences and subjective opinions. In a previous book, "From Opinion Mining to Financial Argument Mining" (Springer, 2021), the first author discussed understanding financial documents in a fine-grained manner, particularly those containing opinions. The book highlighted several future directions, such as financial argument mining, multimodal opinion understanding, and analysis generation, and anticipated a lengthy journey for these topics. However, since 2022, ChatGPT and large language models (LLMs) have shown promising advancements, motivating the authors to write this second book on the topic of financial Natural Language Processing (NLP). Agent-based AI systems have been widely discussed since the advent of LLMs. This book aims to equip researchers and practitioners with the latest methodologies, concepts, and frameworks for developing, deploying, and evaluating AI agents with capabilities in multimodal understanding, decision-making, and interaction. It places a special emphasis on human-centered decision-making and multi-agent cooperation in financial applications. The book surveys the current landscape and discuss future research and development directions. Targeting a wide audience, from students to seasoned researchers in AI and finance, this book offers an overview of recent trends in Agent AI for finance. It provides a foundation for students to understand the field and design their research direction, while inviting experienced researchers to engage in discussions on open research questions informed by pilot experimental results. Although this book focuses on financial applications, the discussed concepts and methods can also be applied to other real-world applications by integrating domain-specific characteristics. The authors look forward to seeing new findings and more novel extensions based on the proposed ideas.
540			_aCreative Commons Attribution 4.0 International _fCC BY 4.0 _uhttp://creativecommons.org/licenses/by/4.0/
588	0		_aOnline resource; title from PDF title page (SpringerLink, viewed July 30, 2025).
650		0	_aArtificial intelligence _xFinancial applications. _0http://id.loc.gov/authorities/subjects/sh2020000035
650		0	_aData mining. _0http://id.loc.gov/authorities/subjects/sh97002073
655		0	_aElectronic books.
700	1		_aTakamura, Hiroya, _eauthor.
830		0	_aSpringerBriefs in intelligent systems. _pArtificial intelligence, multiagent systems, and cognitive robotics, _x2196-5498
856	4	0	_uhttps://link.springer.com/book/10.1007/978-3-031-94687-5 _ySpringer Nature
856	4	0	_uhttps://directory.doabooks.org/handle/20.500.12854/165872 _yFull text is available at the Directory of Open Access Books. Click here to view.
942			_2ddc _cOA