Automated negotiation is an important class of problem that has wide reaching application in the real world. While a lot of work has been done in Agent-Agent negotiations, Human-Agent negotiations have been relatively unexplored. Human- Agent multi issue bilateral negotiations deals with autonomous agents negotiating with humans over more than one item. Designing agents which can engage in such negotiations requires estimating the preferences of the human opponent in real time and proposing offers which are likely to be accepted be- fore the session timeout. We design an agent that estimates the human opponent’s preferences using two new heuristics, Most Changed Least Preferred and Most Offered Most Preferred. Also, the agent utilises the Thomas-Kilmann Conflict Mode Instrument to judge the negotiation strategy of the opponent and then adapts its own strategy to reach agreement faster. The agent does so without the use of historical data, therefore, remaining free from the problems that arise from lack of or biased historical data. Our results show that the agent reaches good agreements against a wide variety of human negotiators. The agreements fall on or near the Pareto-Optimal frontier with the probability of an agreement resulting in an optimal distribution being 97.7%.