More holidaymakers are turning to AI when planning or booking their trips, according to travel association ABTA. The body found that 8% of travellers were using AI - up from 4% last year - with younger holidaymakers more likely to use the technology when planning their trips. However, AI still lagged a long way behind more established methods - such as general internet searches and asking family and friends. Overall, the number of people taking a holiday continued a recent trend of climbing back towards pre-pandemic levels, ABTA said. The travel body described the increase in customers using AI as both a challenge and an opportunity.

Integrating argumentation mechanisms into negotiation dialogue systems improves conflict resolution through exchanges of arguments and critiques. Moreover, incorporating personality attributes enhances adaptability by aligning interactions with individuals' preferences and styles. To advance these capabilities in negotiation dialogue systems, we propose a novel Personality-driven Argumentation-based Negotiation Dialogue Generation (PAN-DG) task. To support this task, we introduce PACT, a dataset of Personality-driven Argumentation-based negotiation Conversations for Tourism sector. This dataset, generated using Large Language Models (LLMs), features three distinct personality profiles, viz. Argumentation Profile, Preference Profile, and Buying Style Profile to simulate a variety of negotiation scenarios involving diverse personalities. Thorough automatic and manual evaluations indicate that the dataset comprises high-quality dialogues. Further, we conduct comparative experiments between pre-trained and fine-tuned LLMs for the PAN-DG task. Multi-dimensional evaluation demonstrates that the fine-tuned LLMs effectively generate personality-driven rational responses during negotiations. This underscores the effectiveness of PACT in enhancing personalization and reasoning capabilities in negotiation dialogue systems, thereby establishing a foundation for future research in this domain.

In recent years, large language model (LLM)–based agents have garnered widespread attention across various fields. Their impressive capabilities, such as natural language communication,21,23 instruction following,26,28 and task execution,22,38 have the potential to expand both the format of information carriers and the way in which information is exchanged. LLM-based agents can now evolve into domain experts, becoming novel information carriers with domain-specific knowledge.1,28 For example, a Travel Agent can retain travel-related information within its parameters. LLM-based agents are also showcasing a new form of information exchange, facilitating more intuitive and natural interactions with users through dialogue and task execution.24,34 Figure 1 shows an example of these capabilities, in which users engage in dialogue with a Travel Agent to obtain information and complete their travel plans.

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How are LLM-based agents used in the future? While many of the existing work on agents has focused on improving the performance of a specific family of objective and challenging tasks, in this work, we take a different perspective by thinking about full delegation: agents take over humans' routine decision-making processes and are trusted by humans to find solutions that fit people's personalized needs and are adaptive to ever-changing context. In order to achieve such a goal, the behavior of the agents, i.e., agentic behaviors, should be evaluated not only on their achievements (i.e., outcome evaluation), but also how they achieved that (i.e., procedure evaluation). For this, we propose APEC Agent Constitution, a list of criteria that an agent should follow for good agentic behaviors, including Accuracy, Proactivity, Efficiency and Credibility. To verify whether APEC aligns with human preferences, we develop APEC-Travel, a travel planning agent that proactively extracts hidden personalized needs via multi-round dialog with travelers. APEC-Travel is constructed purely from synthetic data generated by Llama3.1-405B-Instruct with a diverse set of travelers' persona to simulate rich distribution of dialogs. Iteratively fine-tuned to follow APEC Agent Constitution, APEC-Travel surpasses baselines by 20.7% on rule-based metrics and 9.1% on LLM-as-a-Judge scores across the constitution axes.

Fox News correspondent Bill Melugin gives the latest on Vice President Kamala Harris' southern border visit on'Special Report.' It was George Orwell, in his seminal book "1984," which turned out to be about 40 years off, who wrote that eventually the state would make us believe that 2 2 5. Vice President Kamala Harris' incomprehensibly shameless photo op on the southern border might have dialed it up to a 6. There she was, our vice president, in front of the wall, and the barbed wire, ready to get serious about the problem she and "kind of" President Joe Biden created in the first place. I recalled a film in which Cheech and Chong said it was time to get serious about the band. This is like Oedipus saying, "Who did all this?"

The new kind of Agent-oriented information system, exemplified by GPTs, urges us to inspect the information system infrastructure to support Agent-level information processing and to adapt to the characteristics of Large Language Model (LLM)-based Agents, such as interactivity. In this work, we envisage the prospect of the recommender system on LLM-based Agent platforms and introduce a novel recommendation paradigm called Rec4Agentverse, comprised of Agent Items and Agent Recommender. Rec4Agentverse emphasizes the collaboration between Agent Items and Agent Recommender, thereby promoting personalized information services and enhancing the exchange of information beyond the traditional user-recommender feedback loop. Additionally, we prospect the evolution of Rec4Agentverse and conceptualize it into three stages based on the enhancement of the interaction and information exchange among Agent Items, Agent Recommender, and the user. A preliminary study involving several cases of Rec4Agentverse validates its significant potential for application. Lastly, we discuss potential issues and promising directions for future research.

This is part of Airplane Mode, a series on the business--and pleasure--of travel right now. I'm a travel agent, and when I sat down to write this article, there were 1,154 flight cancellations. Days like this used to be a once-a-year occurrence. A snowpocalypse would smother the entire East Coast or an airline's computer system would go down or a strike would happen. A week of misery would follow while the whole industry dug out.

Artificial intelligence (AI) is transforming the travel industry by offering predictive analytics, tailored recommendations and live updates. The rapid development in AI has made almost all industries curious about this technology. AI is a branch of computer science that is explicitly programmed to create machines that can act intelligent and smarter than humans. With its incredible benefits, AI is offering path-breaking innovations to various industries. Apart from retail, healthcare, finance, and manufacturing, now the travel industry is transforming with AI.

A few weeks ago, I called my broadband provider about intermittent outages. The helpful customer support rep looked at my account and cheerfully told me that I could save money by switching to a different plan. A few minutes later, I had changed my plan to one that cost half as much and delivered comparable speeds. At first, I was happy. Because I realized, in reality, no customer service team is proactively looking out for my well-being before I raise a problem.